Glycan peak attribution

Abstract

In some examples, an apparatus may include a retention time (RT) receiver executed by at least one hardware processor to receive a glucose step standard retention time (RT), and to receive neutral and charged glycans RT. An RT analyzer executed by the at least one hardware processor may determine an adjusted glucose unit (GU) value based on applying the glucose ladder standard RT and the neutral and charged glycans RT to an expected GU value. Further, an expected RT generator executed by the at least one hardware processor may determine an expected RT for a specified glycan based on the adjusted GU value.

Description

Cross-references to related applications

[0001] This application claims priority to co-pending U.S. provisional patent application serial number 63 / 587,593, filed October 3, 2023, entitled “GLYCAN PEAK ASSIGNMENT,” and U.S. non-provisional patent application serial number 18 / 650,999, filed April 30, 2024, entitled “GLYCAN PEAK ASSIGNMENT,” the disclosures of which are incorporated herein by reference in their entirety. Background Technology

[0002] Regarding glycans, releasing glycans from glycoproteins and labeling them with fluorescent markers facilitates the relative quantification of each different glycan structure in the sample. In one example, liquid chromatography (LC) can be used to separate the labeled, released glycans. Attached Figure Description

[0003] Features of this disclosure are illustrated by way of example, not limitation, in the following figures, in which the same numbers denote the same elements, wherein:

[0004] Figure 1 The layout of a glycan peak attribution device is shown as an example according to this disclosure.

[0005] Figure 2 Examples of this disclosure are shown for demonstration purposes. Figure 1 A flowchart illustrating the operation of the glycan peak attribution device and the determination of observed glucose units (GU), expected GU, and predicted GU;

[0006] Figure 3 Examples of this disclosure are shown for demonstration purposes. Figure 1 A flowchart of the scaling process for the operation of the polysaccharide peak attribution device;

[0007] Figure 4 Examples of this disclosure are shown for demonstration purposes. Figure 1 The operation of the glycan peak attribution device is described by fitting to a function to generate a flowchart and associated examples of the hypothetical retention time (RT) for those degrees of polymerization (dps) that are beyond those observed experimentally;

[0008] Figure 5 Examples of this disclosure are shown for demonstration purposes. Figure 1 The operation of the polysaccharide peak attribution device improves the accuracy of RT prediction when adjusting the value;

[0009] Figure 6An example block diagram for glycan peak attribution is shown, based on examples from this disclosure.

[0010] Figure 7 A flowchart illustrating an example method for glycan peak attribution, based on examples from this disclosure, is shown; and

[0011] Figure 8 Another example block diagram for glycan peak attribution is shown, based on another example of this disclosure. Detailed Implementation

[0012] For simplicity and illustrative purposes, this disclosure is described primarily by way of examples. Numerous specific details are set forth in the following description to provide a comprehensive understanding of this disclosure. However, it will be readily apparent that the practice of this disclosure may not be limited to these specific details. In other instances, some methods and structures have not been described in detail to avoid unnecessarily obscuring this disclosure.

[0013] Throughout this disclosure, the terms “a” and “an” are intended to mean at least one of a particular element. As used herein, the term “includes” means including but not limited to, and the term “including” means including but not limited to. The term “based on” means at least partially based on.

[0014] This document discloses a glycan peak attribution device, a method for glycan peak attribution, and a non-transitory computer-readable medium having machine-readable instructions thereon stored thereon for providing glycan peak attribution.

[0015] Assigning peaks to glycan structures after releasing glycans from glycoproteins and labeling them with fluorescent markers can be technically challenging. In some cases, peak retention time (RT) can be used to determine the identity of the glycan structure. However, using retention time can have drawbacks: retention times can vary significantly due to different dates, column lifespan, poorly controlled parameters (such as the effects of ambient temperature), minor variations in the composition of the liquid chromatography (LC) mobile phase, and / or differences between individual columns of the same type.

[0016] Compared to the relatively unstable absolute retention times (RTs) of different oligosaccharides, the selectivity (α) between different oligosaccharides can be relatively stable for separation patterns associated with hydrophilic interaction chromatography (HILIC). In this regard, carbohydrate standards can be used to normalize retention times, thereby creating retention indices (RIs) that can be used to control for the aforementioned variations. RIs for glycan structures can be more stable than retention times and are therefore more suitable for assigning peaks to structures. In one example, peaks can be assigned to glycan structures by referring to a library of expected RIs based on previously encountered separation experiments of glycans with well-established structures.

[0017] In one example, the implementation of RI can rely on running a mixture of glucose homopolymers, such as dextran (e.g., a “ladder”), labeled with the same marker as the sample (e.g., 2-aminobenzamide or InstantPC). Examples of standards (e.g., glucose homopolymer standards labeled with 2-aminobenzamide (2-AB), maltodextrin standards labeled with InstantPC, etc.) can be run as part of the same analytical sequence (e.g., consecutive runs before or after sample runs) and utilize the same instrumentation methods, including mobile phase composition gradients, flow rates, and column temperatures, whenever possible. Results from calibration runs can be used to fit the relationship between RT and the degree of polymerization of the glucose homopolymer using a function such as a fifth-order polynomial. This scheme can be used in both size exclusion chromatography and HILIC mode chromatography for glycans. For each sample run, the same function can be used to map the RT of each glycan peak to a glucose unit (GU) value, which represents the degree of polymerization of the labeled glucose homopolymer that should be eluted simultaneously with the glycan peak in question. In some cases, these can be assumed fractional degrees of polymerization, such as 5.43, 7.81, etc. (e.g., fractional GU falling between the RT values ​​of two peaks in a labeled glucose homopolymer standard).

[0018] If the experimenter is performing a routine analysis and sees a change in RT, implementing the RI protocol can help prevent uncertainty about peak identity during LC runs, as the experimenter recognizes that the shift is due to a change in their system, rather than a new glycan structure appearing in the sample.

[0019] Unknown glycan structures can also be attributed by referring to a list of previously identified (e.g., labeled with the same marker) glycan structures that have been previously shown to give various GU values ​​under very similar experimental conditions. Such a list of glycan structures and their GUs can be referred to as a GU library or GU database, such as the one known as Glycobase. In some HILIC methods, glycan structures on amide HILIC columns with common markers can be eluted in the range of 4 GU to 14 GU, but this range can be higher or lower depending on the column and technique used.

[0020] The same general principle can be used with electrophoresis methods, where the migration time of labeled homopolymer standards can be used to create a migration index that achieves the same goal as the retention index. Tables of known structures and their corresponding GU values ​​can be used to assign glycan structures with neutral labels (such as 2-aminobenzamide), but this may be less accurate and therefore less useful when using positively charged labels such as InstantPC. This is due to the higher charge heterogeneity of such samples. For example, unlike homopolymer mixtures with uniform positive charges, glycan samples containing structures with acidic groups can have a net zero charge or a negative charge when labeled with positively charged labels. In such cases, GU values ​​may be more sensitive to changes in ion interactions caused by experimental conditions that are not easily controlled, such as individual column characteristics, column age, and precise mobile phase composition. Regardless of the reason, in the case of positively charged labels, the approach of assigning unknown peaks based on their GU values ​​may be less reliable.

[0021] To at least address the aforementioned technical challenges, the apparatus, methods, and non-transitory computer-readable media disclosed herein adjust the expected GU (or RT) application for each glycan structure based on the number of acidic monosaccharides contained in each glycan structure in the library (which can be 0, 1, 2, 3, or 4). These “charged groups” of the glycans are known as S0, S1, S2, S3, and S4, respectively. The magnitude of the adjustment applied to the GU (or expected RT) can be determined using the RT of a labeled glycan with a known structure, either as a neighboring external RT standard or as an internal RT standard if some peaks in the sample itself have been reliably assigned. For example, the observed GU of an S0 glycan (such as G0F) can be determined, and the deviation from the expected value (from the “library” of expected GU values) can then be applied as an adjustment to the predicted GU / RT of all other S0 glycans.

[0022] Regarding S2 glycans, the observed deviation of the GU / RT for the observed S2 glycan peak of a known structure can be determined, and this deviation can be used as an adjustment for the predicted GU / RT of all other S2 glycans. This can be done for all charged groups or even other types of structural glycan classes (such as high-mannose glycans).

[0023] However, to limit the burden of obtaining many calibration points, the effective adjustment values ​​for S1, S3, and S4 glycans can be determined by amplifying or reducing the adjustment from S0 or S2 glycans.

[0024] The apparatus, methods, and non-transitory computer-readable medium disclosed herein allow users to input observed RTs for their glucose homopolymer standards, and RTs for S0 and S2 glycans. A library of GU values ​​can be provided for labeled glycans that have been experimentally determined using HILIC columns and separation methods. When the apparatus is operated after a user submits a calibration retention time, the apparatus can determine a predicted RT at which the user should expect to see each glycan in the library on their own system.

[0025] For the apparatus, methods, and non-transitory computer-readable media disclosed herein, in one example, the resulting expected RT library can be automatically (e.g., without human intervention) formatted as a compound database or a .CSV file and imported into a liquid chromatography (LC) / mass spectrometry (MS) data analysis software package (such as MasshunterBioconfim). Using this database, the software package can automatically assign signals observed in LC / MS sample data to said glycan structures based on both the RT and the mass-to-charge ratio of the glycan structure as detected by the mass spectrometer, thereby providing a relatively higher degree of confidence in peak assignment compared to schemes using RT or mass-to-charge ratio alone. For example, as disclosed herein, the calculated expected RT can be exported as a .CSV file containing, in comma-separated column order: the chemical formula of each glycan structure, the expected RT for each glycan in the library, a blank column, the name of the glycan structure, and a final column for annotation. For example, this .CSV file can be loaded into the data analysis software package as a compound database file. The data analysis software package can be used to automatically search for signals in LC / MS data that match the expected mass of compounds in a compound database. A table of detected compounds along with a deterministic score can be output, where the deterministic score is based on the closeness of the observed mass for a given compound to the expected mass based on the chemical formula. This allows users to quickly identify compounds for signals observed in their LC / MS data. The data analysis software package can include additional features that can be used when the compound database includes accurate expected RTs. In that case, the deterministic score can be based on the observed RT of the proposed compound and the expected RT. In the case of MassHunter Bioconfirm, the RT deterministic score can be a linear function, where 0 deviation gives a maximum score of 100. The score linearly decreases to zero when the deviation from the expected RT reaches a value that can be set by the user. The ability to easily score accuracy or compare RTs to their expected values ​​is particularly useful for glycans with more than one isomer that cannot be distinguished by mass but can instead be distinguished by RT. The output table can also display the observed RT versus the expected RT for each signal, allowing the user to see whether the deviation is low enough or large enough to raise suspicion that the proposed compound is incorrect. Therefore, the highly accurate RT predictions achieved through the apparatus, methods, and non-transitory computer-readable media disclosed herein greatly enhance the accuracy and certainty when using data analysis software packages to automatically analyze LC / MS data from glycan samples.

[0026] The apparatus, methods, and non-transitory computer-readable media disclosed herein provide technical benefits such as an air gap separating a user's personal data from the device's software platform. A user may collect both confidential and unconfidential experimental data, but only the unconfidential data needs to be input into the software platform. This data is then processed by the software platform and returned to the user as a list of predicted retention times, which helps the user interpret their data. Furthermore, the apparatus, methods, and non-transitory computer-readable media disclosed herein provide the ability to predict retention times, eliminating the need to understand how to compare GU values ​​to assign peak identities. In one example disclosed herein, the ability to predict retention times is achieved by using a novel polynomial that expresses retention times as a function of GU. This polynomial can be paired with novel extrapolation features as disclosed herein. The implementation of glycan adjustment values ​​as disclosed herein provides another technical advancement that improves the accuracy of retention time prediction and allows for calibration improvements using internal sample peaks.

[0027] The apparatus, methods, and non-transitory computer-readable media disclosed herein typically involve first generating GU values ​​using glucose homopolymer standards and then adjusting them using actual glycan RT. In this respect, the apparatus, methods, and non-transitory computer-readable media disclosed herein can be implemented based on glycan RT.

[0028] According to the apparatus, method, and another aspect of the non-transitory computer-readable medium disclosed herein, some glycans elute at retention times greater than the retention time of the highest degree of polymerization peak, and the highest degree of polymerization peak can be reliably integrated using available standards of labeled glucose homopolymers. This presents a technical challenge when interpolating according to a 5th-order polynomial, which is valid from the lowest degree of polymerization up to the highest degree of polymerization used to fit the function. In this regard, hypothetical RTs for degrees of polymerization beyond those observed experimentally can be generated by fitting a function (such as a linear function) based on the final two or three points (e.g., the RTs of the highest, second-highest, and third-highest degree of polymerization labeled glucose homopolymer peaks). These hypothetical glucose homopolymer retention times can be considered as those observed experimentally, thus allowing the 5th-order polynomial to be extended to higher degrees of polymerization (and therefore higher GU / RT) than would otherwise be possible.

[0029] For the devices, methods, and non-transitory computer-readable media disclosed herein, the elements of the devices, methods, and non-transitory computer-readable media disclosed herein can be any combination of hardware and programming to implement the functionality of the respective elements. In some examples described herein, the combination of hardware and programming can be implemented in a variety of different ways. For example, the programming for the elements can be processor-executable instructions stored on a non-transitory machine-readable storage medium, and the hardware for the elements can include processing resources for executing these instructions. In these examples, a computing device implementing such elements can include a machine-readable storage medium storing instructions and processing resources for executing the instructions, or the machine-readable storage medium can be stored and accessed separately by the computing device and the processing resources. In some examples, some elements can be implemented in a circuit system.

[0030] Figure 1 The layout of the example glycan peak attribution device (also referred to below as "device 100") is shown.

[0031] refer to Figure 1 Device 100 may include a retention time (RT) receiver 102, the retention time receiver being powered by at least one hardware processor (e.g., Figure 6 Hardware processor 602, and / or Figure 8 The hardware processor 804 executes to receive glucose step standard retention time (RT) 104. Further, the RT receiver 102 can receive neutral and charged glycan RT 106.

[0032] Composed of at least one hardware processor (e.g., Figure 6 Hardware processor 602, and / or Figure 8 The RT analyzer 108, executed by the hardware processor 804, can determine the adjusted GU value 110 based on applying glucose ladder standard RT 104 and neutral and charged polysaccharide RT 106 to the expected glucose unit (GU) value.

[0033] Composed of at least one hardware processor (e.g., Figure 6 Hardware processor 602, and / or Figure 8 The expected RT generator 112, executed by the hardware processor 804, can determine the expected RT 114 for a given glycan based on the adjusted GU value 110.

[0034] Composed of at least one hardware processor (e.g., Figure 6 Hardware processor 602, and / or Figure 8 The compound identifier 128, executed by the hardware processor 804, can identify at least one compound 130 based on the expected RT 114.

[0035] According to the examples disclosed herein, the RT analyzer 108 can determine the adjusted GU value 110 by applying the glucose ladder standard RT 104 and the neutral and charged polysaccharide RT 106 to the expected GU value, and by determining the fifth-order polynomial GU as a function of RT based on the glucose ladder standard RT 104.

[0036] According to the examples disclosed herein, the RT analyzer 108 can determine the adjusted GU value 110 by applying the glucose ladder standard RT 104 and the neutral and charged glycan RT 106 to the expected GU value, and by determining the observed GU 116 based on the fifth-order polynomial GU and the neutral and charged glycan RT.

[0037] According to the examples disclosed herein, the RT analyzer 108 can determine the adjusted GU value 110 by applying a glucose ladder standard RT 104 and neutral and charged glycans RT 106 to the expected GU value, and by determining a GU adjustment factor 118 based on the observed GU 116. Furthermore, the RT analyzer 108 can determine the adjusted GU value 110 by applying the GU adjustment factor 118 to the expected GU value.

[0038] According to the examples disclosed herein, the GU adjustment factor 118 may include a first value representing a neutral glycan adjustment value that may be selected from a set of options available in the GU library. In some cases, this would allow adjustment to be performed using glycans in the sample for which their identity is known. Since the adjustment is performed using data from a sample run, accuracy can be further improved by eliminating run-to-run variations from this calibration aspect. The GU adjustment factor 118 may include a second value representing an acidic glycan adjustment value applied to glycan S2, which may similarly be selected from a set of options available in the GU library. This would similarly allow adjustment to be performed using glycans in the sample for which their identity is known. The GU adjustment factor 118 may include a third value representing an acidic glycan adjustment value multiplied by a factor of 0.5 and applied to glycan S1. The GU adjustment factor 118 may include a fourth value representing an acidic glycan adjustment value multiplied by a factor of 1.5 and applied to glycan S3. The GU adjustment factor 118 may include a fifth value, which represents the acidic glycan adjustment value multiplied by a factor of 2.0 and applied to S4 glycan.

[0039] According to the examples disclosed herein, the RT analyzer 108 can determine the adjusted GU value 110 by applying glucose ladder standard RT 104 and neutral and charged polysaccharide RT 106 to the expected GU value, by subtracting the GU adjustment factor from the corresponding expected GU value.

[0040] According to the examples disclosed herein, the RT analyzer 108 can determine the adjusted GU value 110 by applying a glucose step standard RT 104 and neutral and charged polysaccharides RT 106 to the expected GU value, and by determining a fifth-order polynomial RT as a function of GU based on the glucose step standard RT.

[0041] Based on the examples disclosed herein, the expected RT 114 can be exported as, for example, a .CSV file 120, which contains, in comma-separated column order: the chemical formula of each glycan structure (e.g., glycan formula 122), the expected RT 114 for each glycan in the library, a blank column, the name of the glycan structure (e.g., glycan name 124), and a final column for annotation. This .CSV file 120 can be formatted, for example, by an LC / MS data analysis formatter 126 and loaded by a compound identifier 128 as a compound database file in a data analysis software package (such as MassHunter Data Analysis or Masshunter Bioconfirm). The data analysis software package can be used by the compound identifier 128 to automatically search for signals in the LC / MS data that match the expected quality of compounds in the compound database. For example, the .CSV file 120 can be loaded as a compound library into the data analysis software package for automated analysis of LC / MS data. The LC / MS data can be generated by LC / MS acquisition software and an LC / MS instrument used to analyze the user's samples. Examples of compounds may include InstantPC (IPC) labeled glycan structures G0F, G2F, G2S2, Man5, and Man6. Other examples may include 2AB-labeled glycan structures if the user is using 2AB-labeled sample pretreatment instead of IPC.

[0042] Compound identifier 128 can output a table of detected compounds (e.g., including compound 130) along with a deterministic score, where the deterministic score can be based on how closely the observed quality fits the expected quality based on the chemical formula for a given compound. The deterministic score can range from 0 to 100, where 100 indicates the best possible match between the expected and observed values. In this respect, compound identifier 128 can utilize a scalable system where the expected deviation can be edited in software (e.g., to account for different experimental settings with different levels of reproducibility), which affects the rate at which the score decreases from 100 when the observed value deviates from the expected value. Furthermore, the expected quality can be determined by compound identifier 128 based on the chemical formula of the compound and can be independent of the RT (Resolution Time).

[0043] The deterministic score can be used as an indicator to identify compounds whose signals are observed in LC / MS data for the user. Thus, by utilizing the deterministic score, the compound identifyer 128 can identify at least one compound 130 based on the expected RT 114.

[0044] Figure 2 A flowchart illustrating the use of the device 100 to demonstrate operation and determine observed glucose units (GU), expected GU, and predicted GU is shown in the example of this disclosure.

[0045] refer to Figure 2 At 200, device 100 can apply an adjustment to the expected GU (or RT) of each glycan structure (e.g., via RT analyzer 108) based on the number of acidic monosaccharides contained in each glycan structure in the library of expected GU / RT values ​​at 202. These “charged groups” of the glycans are known as S0, S1, S2, S3, and S4, respectively. The adjustment applied to the GU (or expected RT) by RT analyzer 108 at 204 (regarding...) Figure 3 (Further details provided) The magnitude can be determined using the RT of labeled glycans with known structures, either as neighboring external RT standards or as internal RT standards where some peaks in the sample itself have been reliably assigned. For example, the observed GU of S0 glycans (such as G0F) can be determined and the deviation from the expected value (from a "library" of expected GU values) can then be applied as an adjustment to the predicted GU / RT of all other S0 glycans. In this respect, Figure 2 A flowchart illustrating the use of the device 100 to demonstrate operation and determine observed glucose units (GU), expected GU, and predicted GU is shown in the example of this disclosure.

[0046] Specifically, refer to Figure 2 Regarding the user task at 206, at 208, the user can run a glucose standard, which can be used for the glucose ladder standard RT at box 210. At box 212, the glucose ladder standard RT is further received by the RT analyzer 108 to determine the 5th-order polynomial GU = f(RT). At box 214, the RT analyzer 108 can utilize the output from box 212 to determine the GU (observed GU) using equation f.

[0047] At 216, the user can run a glycan sample, which at box 218 can be used for a neutral glycan RT and a charged glycan RT. At box 214, both glycan RTs are also received by the RT analyzer 108 to determine the GU (observed GU) using equation f.

[0048] At box 220, RT analyzer 108 can further receive glucose ladder standard RT at box 210 to determine the 5th degree polynomial RT = g(GU).

[0049] The operations in boxes 212, 214, and 220, as well as those in boxes 200, 202, 204, 224, 226, and 228, can be performed at the server at 222.

[0050] At box 224, RT analyzer 108 can use the outputs of boxes 212, 214, and 220 to determine RT using equation g.

[0051] The determined RT from box 224 can be used together with equation f (e.g., box 212), and at box 226, the difference between the result and the predicted GU can be determined. This difference can be divided by the slope (first derivative) of equation f at the x-value RT (calculated at box 224) to generate an adjustment value at box 226, which is then subtracted from the RT in box 224 to determine the expected RT 114 at box 228 (e.g., via the expected RT generator 112). If needed, the generated RT (box 228) can be iteratively fed back through the process in box 226 to generate increasingly accurate expected RTs.

[0052] Regarding determining the expected RT 114 at box 228, referring to box 234, in an alternative approach, the GU value can be input into equation f to solve for RT (e.g., GU = f(RT)). In this respect, the implementation at box 224 can represent a first approximate representation of RT, and the adjustment at box 226 can represent an improvement on this approximate representation. This alternative approach also eliminates boxes 220, 224, and 226. Moreover, the cyclic arrow between boxes 226 and 228 indicates that the adjustment in box 226 can be performed multiple times to obtain a better approximate representation (e.g., an iterative scheme as disclosed herein).

[0053] Furthermore, at box 230, the expected RT 114 can be provided to the user.

[0054] Device 100 provides an air gap 232 that separates the user's personal data (e.g., at 206) from the software platform (e.g., at 222). The user may collect both confidential and unconfidential experimental data, but they only need to input the unconfidential data into the software platform for device 100. This data can be processed by the software platform and returned to the user as a list of predicted response times (RTs), which helps the user interpret their data.

[0055] Figure 3A flowchart illustrating the scaling process of device 100, based on an example of this disclosure, is shown.

[0056] Figure 3 The text further describes in detail Figure 2 The adjustment at box 204 is applied by RT analyzer 108. (Reference) Figure 3 For S2 glycans, the observed deviation of the GU / RT for the observed S2 glycan peak of a known structure can be determined, and this deviation can be used as an adjustment for the predicted GU / RT of all other S2 glycans. This can be performed for all charged groups, or even other types of structural glycans such as high-mannose glycans.

[0057] However, to limit the burden of obtaining many calibration points, the effective adjustment values ​​for S1, S3, and S4 glycans can be determined by amplifying or reducing the adjustment from S0 or S2 glycans.

[0058] Device 100 thus allows the user to input observed RTs for their glucose homopolymer standards, and RTs for S0 and S2 glycans. A library of GU values ​​can be provided for labeled glycans that have been experimentally determined using HILIC columns and separation methods. When device 100 is operated after the user submits a calibration RT, the device can determine a predicted RT at which the user should expect to see each glycan in the library on their own system.

[0059] refer to Figure 3 At position 300, you can... Figure 2 The GU adjustment factor is obtained in box 204. The GU adjustment factor can be used for the acidic glycan adjustment value in box 302 and for the neutral glycan adjustment value in box 304. The neutral glycan adjustment value in box 304 can represent a first value. The acidic glycan adjustment value in box 302 can be used to determine the adjusted GU value 110. For example, the acidic glycan adjustment value in box 302 can be used to determine a second value in box 306 based on applying it as is to S2 (bis-sialylated) glycan. The acidic glycan adjustment value in box 302 can be used to determine a third value in box 308 based on multiplying by 0.5 and applying it to S1 glycan. The acidic glycan adjustment value in box 302 can be used to determine a fourth value in box 310 based on multiplying by 1.5 and applying it to S3 glycan. The acidic glycan adjustment value in box 302 can be used to determine a fifth value in box 312 based on multiplying by 2 and applying it to S4 glycan. These first through fifth values ​​can be subtracted from their corresponding library GU values ​​to determine the adjusted GU value 110.

[0060] Figure 3 Examples of how the process is implemented are in Figure 3As shown in Table 314. For example, the neutral glycan adjustment value (e.g., the first value) at box 304 is specified as 0.121, the acid glycan adjustment value (e.g., the second value) applied as is to glycan S2 at box 306 from box 302 is specified as 0.214, the third value from box 308 is specified as 0.107, the fourth value from box 310 is specified as 0.321, and the fifth value from box 312 is specified as 0.428. In this respect, as shown at 316, the adjusted GU corresponding to glycans 1 to 10 is shown.

[0061] Figure 4 The document presents a flowchart and related examples illustrating the operation of device 100 by generating a hypothetical retention time (RT) for a degree of aggregation beyond those observed experimentally, through fitting to a function, according to examples in this disclosure.

[0062] refer to Figure 4 For the device 100 disclosed herein, some glycans elute at RTs greater than the RT of the highest degree of polymerization peak, and the highest degree of polymerization peak can be reliably integrated using available standards of labeled glucose homopolymers. This presents a technical challenge when interpolating according to a 5th-order polynomial, which is valid from the lowest degree of polymerization up to the highest degree of polymerization used to fit the function. In this regard, hypothetical RTs for degrees of polymerization beyond those observed experimentally can be generated by fitting a function (such as a linear function) based on the RTs of the final two or three points (e.g., the highest, second highest, and third highest degree of polymerization labeled glucose homopolymer peaks). These hypothetical glucose homopolymer retention times can be considered as they are observed experimentally, thus allowing the 5th-order polynomial to extend to higher degrees of polymerization (and therefore higher GU / RT) than would otherwise be possible. In this respect, Figure 4 The document presents a flowchart and related examples illustrating the operation of device 100 by generating a hypothetical retention time (RT) for the degree of aggregation (dps) exceeding those observed experimentally, through fitting to a function, according to examples in this disclosure.

[0063] refer to Figure 4 At box 400, regarding the extrapolation, the RT for the last three most recent glucose stepwise standard peaks can be used to fit to the logarithmic equation (e.g., The result from box 400 can be received by the extrapolation function at box 402. The extrapolation function at box 402 can further receive GU values ​​greater than the last glucose peak from box 404, and generate extrapolated RT values ​​for the colloidal sugar at box 406.

[0064] An example plot of glucose step ladder standard data is shown at 408. The plot of glucose step ladder standard data shows a 5th-degree polynomial y = 4E-07x. 5 - 5E-05x 4 + 0.0032x 3 - 0.1185x 2 + 2.8982x - 5.0559. Furthermore, the graph of the glucose ladder standard data shows the logarithmic equation y = 13.563ln(x) - 16.487.

[0065] Figure 5 An example based on this disclosure demonstrates the improvement in RT prediction accuracy when implementing the adjusted value in the operation of device 100.

[0066] refer to Figure 5 This demonstrates that when the adjusted values ​​disclosed herein are achieved, the RT prediction error is reduced by % which helps to accurately identify chromatographic peaks.

[0067] Figures 6 to 8 The following diagrams illustrate example block diagram 600 for glycan peak attribution, flowcharts of example method 700, and another example block diagram 800. Block diagrams 600, method 700, and block diagram 800 are provided above for reference as examples rather than limitations. Figure 1 The described method is implemented on device 100. Block diagram 600, method 700, and block diagram 800 can be implemented in other devices. In addition to block diagram 600, Figure 6 Hardware of device 100 capable of executing the instructions of block diagram 600 is also shown. The hardware may include processor 602 and memory 604 storing machine-readable instructions that, when executed by the processor, cause the processor to execute the instructions of block diagram 600. Memory 604 may represent a non-transitory computer-readable medium. Figure 7 An example method for assigning glycan peaks can be described, along with the steps of that method. Figure 8 This can be represented by a non-transitory computer-readable medium 802 on which machine-readable instructions for providing glycan peak attribution are stored, as in the example. When these machine-readable instructions are executed, they cause processor 804 to execute... Figure 8 The instructions in block diagram 800 are shown in the figure.

[0068] Figure 6 Processor 602 and / or Figure 8 The processor 804 may include one or more processors or other hardware processing circuitry to perform the methods, functions, and other processes described herein. These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium, which may be non-transitory (e.g., Figure 8 Non-transitory computer-readable media 802, such as hardware storage devices (e.g., RAM (random access memory), ROM (read-only memory), EPROM (erasable programmable ROM), EEPROM (electrically erasable programmable ROM), hard disk drives, and flash memory). Memory 604 may include RAM, in which machine-readable instructions and data for the processor may reside during runtime.

[0069] refer to Figures 1 to 6 And specifically refer to Figure 6 As shown in block diagram 600, memory 604 may include instructions 606 to receive glucose step standard retention time (RT) 104.

[0070] Processor 602 can acquire, decode, and execute instructions 608 to receive neutral and charged polysaccharide RT 106.

[0071] The processor 602 can acquire, decode, and execute instructions 610 to determine an adjusted GU value 110 based on applying glucose ladder standard RT 104 and neutral and charged polysaccharides RT 106 to the expected glucose unit (GU) value.

[0072] Processor 602 can acquire, decode, and execute instruction 612 to determine the expected RT 114 for a given glycan based on the adjusted GU value 110.

[0073] The processor 602 can acquire, decode, and execute instructions 614 to identify at least one compound 130 based on the expected RT.

[0074] refer to Figures 1 to 5 and Figure 7 And especially Figure 7 For method 700, at block 702, the method may include receiving at least one glucose step standard retention time (RT) 104 and at least one neutral and charged polysaccharide RT 106.

[0075] At box 704, the method may include determining at least one GU adjustment factor 118 based on applying at least one glucose ladder standard RT 104 and at least one neutral and charged polysaccharide RT 106 to at least one expected glucose unit (GU) value.

[0076] At box 706, the method may include determining at least one adjusted GU value 110 based on at least one GU adjustment factor 118.

[0077] At box 708, the method may include determining at least one expected RT 114 for at least one specified glycan based on at least one adjusted GU value 110.

[0078] refer to Figures 1 to 5 and Figure 8 And especially Figure 8 For block diagram 800, non-transitory computer-readable medium 802 may include instructions 806 to determine an adjusted GU value 110 based on the retention time (RT) 104 of glucose ladder standard and the neutral and charged polysaccharide RT 106 applied to the expected glucose unit (GU) value.

[0079] Processor 804 can acquire, decode, and execute instructions 808 to determine the expected RT 114 for a given glycan based on the adjusted GU value 110.

[0080] The examples described and illustrated herein are examples, along with some variations thereof. The terminology, descriptions, and figures used herein are set forth by way of illustration only and are not intended to be limiting. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims and their equivalents, wherein, unless otherwise stated, all terms are meant in their broadest reasonable sense.

Claims

1. A glycan peak attribution device, comprising: A retention time (RT) receiver, which is executed by at least one hardware processor, to: Retention time (RT) of glucose step ladder standard; as well as Receives neutral and charged polysaccharides RT; An RT analyzer, executed by the at least one hardware processor, to: The adjusted GU value is determined by applying the glucose step standard RT and the neutral and charged polysaccharide RT to the expected glucose unit (GU) value. An expected RT generator, which is executed by the at least one hardware processor, to: The expected RT for a given glycan is determined based on the adjusted GU value; as well as A compound identifier, executed by the at least one hardware processor, to: At least one compound was identified based on the expected RT.

2. The glycan peak attribution device of claim 1, wherein the RT analyzer is executed by the at least one hardware processor to determine the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value by: Based on the glucose ladder standard RT, the fifth-order polynomial GU, which is a function of RT, is determined.

3. The glycan peak attribution device of claim 2, wherein the RT analyzer is executed by the at least one hardware processor to determine the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value by: The observed GU is determined based on the fifth-order polynomial GU and the neutral and charged glycans RT.

4. The glycan peak attribution device of claim 3, wherein the RT analyzer is executed by the at least one hardware processor to determine the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value by: The GU adjustment factor is determined based on the observed GU; and The adjusted GU value is determined by applying the GU adjustment factor to the expected GU value.

5. The polysaccharide peak attribution device according to claim 4, wherein the GU adjustment factor includes a first value, the first value representing a neutral polysaccharide adjustment value.

6. The glycan peak attribution device according to claim 4, wherein the GU adjustment factor includes a second value, the second value representing the acid glycan adjustment value applied to S2 glycan.

7. The glycan peak attribution device according to claim 4, wherein the GU adjustment factor includes a third value, the third value representing an acid glycan adjustment value multiplied by a factor of 0.5 and applied to S1 glycan.

8. The glycan peak attribution device according to claim 4, wherein the GU adjustment factor includes a fourth value, the fourth value representing an acid glycan adjustment value multiplied by a factor of 1.5 and applied to S3 glycan.

9. The glycan peak attribution device according to claim 4, wherein the GU adjustment factor includes a fifth value, the fifth value representing an acid glycan adjustment value multiplied by a factor of 2.0 and applied to S4 glycan.

10. The glycan peak attribution device of claim 4, wherein the RT analyzer is executed by the at least one hardware processor to determine the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value by: The adjusted GU value is determined by subtracting the GU adjustment factor from the corresponding expected GU value.

11. The glycan peak attribution device of claim 1, wherein the RT analyzer is executed by the at least one hardware processor to determine the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value by: Based on the glucose ladder standard GU, the fifth-order polynomial RT, which is a function of RT, is determined.

12. A method for assigning glycan peaks, the method comprising: The retention time (RT) of at least one glucose step standard and the RT of at least one neutral and charged polysaccharide are received by at least one hardware processor. The at least one hardware processor determines at least one GU adjustment factor based on applying the at least one glucose ladder standard RT and the at least one neutral and charged polysaccharide RT to at least one expected glucose unit (GU) value; as well as The at least one hardware processor determines at least one adjusted GU value based on the at least one GU adjustment factor; as well as The at least one hardware processor determines at least one expected RT for at least one specified glycan based on the at least one adjusted GU value.

13. The method of claim 12, wherein determining the at least one adjusted GU value by the at least one hardware processor based on the at least one GU adjustment factor further comprises: The at least one adjusted GU value is determined by subtracting the at least one GU adjustment factor from the at least one expected GU value.

14. A non-transitory computer-readable medium storing machine-readable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to: The adjusted GU value was determined by applying the retention time (RT) of glucose step ladder standards and the RT of neutral and charged polysaccharides to the expected glucose unit (GU) value. Based on the adjusted GU value, the expected RT for the specified glycan is determined.

15. The non-transitory computer-readable medium of claim 14, wherein the machine-readable instructions for determining the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value, when executed by the at least one hardware processor, further cause the at least one hardware processor to: The fifth-order polynomial GU, which is a function of RT, is determined based on the glucose ladder standard RT.

16. The non-transitory computer-readable medium of claim 15, wherein the machine-readable instructions for determining the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value, when executed by the at least one hardware processor, further cause the at least one hardware processor to: The observed GU is determined based on the fifth-order polynomial GU and the neutral and charged glycans RT.

17. The non-transitory computer-readable medium of claim 16, wherein the machine-readable instructions for determining the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value, when executed by the at least one hardware processor, further cause the at least one hardware processor to: The GU adjustment factor is determined based on the observed GU; and The adjusted GU value is determined by applying the GU adjustment factor to the expected GU value.

18. The non-transitory computer-readable medium of claim 17, wherein the GU adjustment factor comprises: The first value represents the neutral polysaccharide adjustment value; The second value represents the acidic polysaccharide adjustment value applied to S2 polysaccharide; The third value represents the acidic polysaccharide adjustment value multiplied by a factor of 0.5 and applied to S1 polysaccharide; The fourth value represents the acidic polysaccharide adjustment value multiplied by a factor of 1.5 and applied to S3 polysaccharide; as well as The fifth value represents the acidic polysaccharide adjustment value multiplied by a factor of 2.0 and applied to S4 polysaccharide.

19. The non-transitory computer-readable medium of claim 17, wherein the machine-readable instructions for determining the adjusted GU value based on applying the glucose step standard RT and the neutral and charged glycan RT to the expected GU value, when executed by the at least one hardware processor, further cause the at least one hardware processor to: The adjusted GU value is determined by subtracting the GU adjustment factor from the corresponding expected GU value.

20. The non-transitory computer-readable medium of claim 14, wherein the machine-readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: A library for generating the expected RT based on the expected RT for the specified glycan; and Based on the expected RT from the library of expected RTs, the signals observed in liquid chromatography (LC) / mass spectrometry (MS) data will be attributed to the glycan structure.

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