Automated quant workflow

Abstract

A method of performing mass spectrometry can include: for each of a plurality of standard samples containing different concentrations of a target analyte, integrating at least one ion detection peak of the target analyte over a time window defined by an expected delivery time and an initial delivery time window width corresponding to delivery of the target analyte via a delivery device to an ion source of a mass spectrometer so as to obtain integrated peak areas for the standard samples. A calibration curve corresponding to the integrated peak areas as a function of the concentration of the target analyte can be used to identify at least a subset of the standard samples that exhibit an expected variation of their peak areas as a function of target analyte concentration. Sample delivery times of those standard samples can be used to obtain a calibrated sample delivery time and associated window width.

Description

ABS-0863AUTOMATED QUANT WORKFLOWRELATED APPLICATIONS

[0001] This application claims priority to, and the benefit of, U.S. Provisional Application No. 63 / 726,750 filed on December 2, 2024, the contents of which are incorporated herein by reference in their entirety.TECHNICAL FIELD

[0002] The present disclosure relates generally to mass spectrometry and in particular to methods and systems for analysis of ion detection data generated by mass spectrometric systems.BACKGROUND

[0003] Mass spectrometry (MS) is an analytical technique for determining the structure of chemical substances with both qualitative and quantitative applications. MS can be useful for identifying unknown compounds, determining the composition of atomic elements in a molecule, determining the structure of a compound by observing its fragmentation, and quantifying the amount of a particular chemical compound in a mixed sample. Mass spectrometers detect chemical entities as ions such that a conversion of the analytes to charged ions must occur. In certain data analysis modes, the analysis of ion detection data in mass spectrometry can involve the detection of one or more peaks, e.g., peaks in a chromatogram, corresponding to a target analyte within a sample and integrating the area under such peaks to arrive at a quantitative measure of the concentration of the target analyte in the sample.

[0004] In conventional methods, such peak integrations in absolute quantitation workflows performed by a software module need to be reviewed by a user to ensure accuracy. For example, an expected retention time associated with the passage of the target analyte through a liquid chromatography (LC) column may be different from the current retention time, leading to a shift of at least some of the target analyte peaks outside of the retention time window. In addition, noise below the limit of quantitation (LOQ) can be commonly reported as a peak, which ideally need to be removed via a user’s inspection. Such review and correction of ion detection peaks can be, however, time consuming.ABS-0863Summary

[0005] In one aspect, a method of performing mass spectrometry is disclosed, which comprises the following steps: for each of a plurality of standard samples containing different known concentrations of a target analyte, integrating at least one ion detection peak associated with said target analyte over a time window defined by an expected delivery time and an initial delivery time window width corresponding to delivery of said target analyte via a delivery device to an ion source of a mass spectrometer so as to obtain an integrated peak area for each of the plurality of standard samples, generating a calibration curve corresponding to said integrated peak areas as a function of the concentration of the target analyte in said standard samples, identifying at least two of said plurality of standard samples that exhibit an expected variation of their integrated peak areas as a function of the target analyte concentration, computing an average delivery time and a measured variation of the delivery time based on the delivery times associated with the identified standard samples, and determining a calibrated delivery time and a calibrated delivery time window width based on said average delivery time and said measured variation of the delivery time for use in integrating one or more ion detection peaks associated with said target analyte in one or more unknown samples.

[0006] In various embodiments, the calibrated delivery time can be substantially equal to the average delivery time. Further, in various embodiments, the calibrated delivery time window width can be determined based on the measured variation of the delivery time, e.g., based on the standard deviation of the delivery times. By way of example and without limitation, the calibrated delivery window width can correspond to a confidence interval in a range of about 90 to about 99 percent around the calibrated delivery time.

[0007] In various embodiments, a variety of sample delivery devices can be employed. By way of example, and without limitation, the delivery device can be any of a liquid chromatography (LC) column, a capillary electrophoresis device, and a gas chromatography device. Again, by way of example, the capillary electrophoresis device can be configured to employ imaged capillary isoelectric focusing (icIEF).

[0008] In various embodiments in which the delivery device is an LC column, the delivery time associated with the LC column can be substantially equal to a retention time (RT) of the target analyte as it passes through the LC column.ABS-0863

[0009] In various embodiments, the method can further include reintegrating the ion detection peaks associated with the target analyte in the standard samples using the average delivery time and a delivery time window width defined based on said measured variation of the delivery time to generate a revised calibration curve.

[0010] Further, in various embodiments, the revised calibration curve is utilized to determine a revised calibration delivery time and a revised calibrated delivery time window width for use in integrating one or more ion detection peaks associated with the target analyte in the one or more unknown samples.

[0011] In various embodiments, the calibration delivery time and the calibration delivery time window width or the revised calibration delivery time and the revised calibration delivery time window width can be utilized to integrate at least one ion detection peak associated with the target analyte in at least one of the one or more unknown samples.

[0012] In various embodiments, the at least two standard samples exhibiting an expected variation of their respective integrated areas as a function of concentration of the target analyte can be identified as those that exhibit a concentration of the target analyte that is within an error margin of the calibration curve. In some such embodiments, the error margin of the calibration curves can be defined based on a standard deviation associated with fitting the integrated peak areas of the standard samples as a function of the concentration of the target analyte to a predefined curve fitting function, e.g., a linear function of the integrated peak areas versus the concentration of the target analyte.

[0013] In various embodiments, each of the standard samples can include a substantially identical, and known concentration of one or more internal standards (IS), and the method can further include the following steps: for each of the standard samples, integrating at least one ion detection peak corresponding to said IS and associated with an expected IS delivery time and a default IS delivery time window width to generate a plurality of IS integrated peak areas, using the plurality of IS integrated peak areas to identify at least two of said plurality of standard samples exhibiting expected IS integrated peak areas, determining an average IS delivery time based on the delivery times associated with said at least two identified standard samples, determining a delivery time shift by comparing said average IS delivery time with the expectedABS-0863IS delivery time, and utilizing the delivery time shift to define the expected delivery time of the target analyte for the standard samples.

[0014] Further, in various embodiments, each of the standard samples as well as each of the one or more unknown samples contains a substantially identical, and known, concentration of the IS and the method further includes the following steps: for each of the standard and the unknown samples, integrating at least one ion detection peak corresponding to the IS and associated with an expected IS delivery time and a default IS delivery time window width to generate a plurality of IS integrated peak areas, using the plurality of IS integrated peak areas to identify at least two of said plurality of the standard and the unknown samples exhibiting expected IS integrated peak areas, determining an average IS delivery time based on the delivery times associated with said at least two identified samples, determining a delivery time shift by comparing said average IS delivery time with the expected IS delivery time, and utilizing the delivery time shift to define the expected delivery time of the target analyte for the standard samples.

[0015] In various embodiments, the expected IS integrated peak area corresponds to a peak area that is substantially uniform across the various samples containing the IS. In various embodiments, an IS peak area that would deviate substantially, e.g., by two standard deviations or more, from the constant value can be identified as an outlier.

[0016] In various embodiments, the initial delivery time window width for the analytes can be set for the standard samples to be narrower than the default IS delivery time window width.

[0017] In various embodiments, the IS is chemically different than the target analyte. Further, in various embodiments, the known concentration of the IS is sufficiently high to allow an unambiguous identification of the at least one ion detection peak of the IS.

[0018] In a related aspect, a computer program product is disclosed, which includes a non- transitory and tangible computer readable storage medium storing a program with instructions for execution on a processor so as to execute a method of performing mass spectrometry, the method comprising: for each of a plurality of standard samples containing different known concentrations of a target analyte, integrating at least one ion detection peak associated with said target analyte over a time window associated with an expected delivery time and an initial delivery time window width corresponding to delivery of said target analyte via a delivery deviceABS-0863 to an ion source of a mass spectrometer so as to obtain an integrated peak area for each of the plurality of standard samples, generating a calibration curve corresponding to said integrated peak areas as a function of the concentration of the target analyte in said standard samples, identifying at least two of the standard samples exhibiting an expected variation of their integrated peak areas as a function of concentration of the target analyte, computing an average delivery time and a measured variation of the delivery time based on the delivery times associated with the at least two identified standard samples, and determining a calibrated delivery time and a calibrated delivery time window width based on said average delivery time and said measured variation of the delivery time for use in integrating one or more ion detection peaks associated with said target analyte in one or more unknown samples.

[0019] In a related aspect, a mass spectrometer is disclosed, which includes an ion source for ionizing a sample containing a target analyte to generate a plurality of ions corresponding to said target analyte, a sample delivery device for delivering the sample to said ion source, a mass analyzer for generating at least one ion detection signal associated with any of said target analyte ions or ions derived from said target analyte ions, wherein said at least one ion detection signal comprises at least one ion detection peak. The mass spectrometer further includes a data analysis module that is configured to: receive ion detection signals corresponding to a plurality of standard samples containing different known concentrations of the target analyte, each of the ion detection signals including at least one ion detection peak associated with the target analyte, for each of the standard samples, integrate the respective at least one ion detection peak over a time window associated with an expected delivery time and an initial delivery time window width corresponding to delivery of said target analyte via said delivery device to the ion source so as to obtain an integrated peak area for each of the plurality of standard samples, generate a calibration curve corresponding to said integrated peak areas as a function of the concentration of the target analyte in said standard samples, identify at least two of said standard samples exhibiting an expected variation of their respective peak areas as a function of the target analyte concentration, compute an average delivery time and a measured variation of the delivery time based on the delivery times associated with the identified standard samples, and determine a calibrated delivery time and a calibrated delivery time window width based on said average delivery time and said measured variation of the delivery time for use in integrating the at least one ion detection peak associated with the target analyte.ABS-0863

[0020] By way of example, and without limitation, in various embodiments of the mass spectrometer, the sample delivery device can be any of an LC column, a capillary electrophoresis device, and a gas chromatography device.

[0021] In various embodiments, the ion detection signal generated by the mass analyzer can be any of an extracted ion chromatogram (XIC), a m / z mass spectrum, etc.

[0022] In various embodiments, the mass spectrometer can further include a mass filter that is positioned between the ion source and the mass analyzer, where the mass filter is configured to allow passage of said target analyte ions therethrough.

[0023] In various embodiments, the mass spectrometer can further include an ion interaction device that is positioned between the ion source and the mass filter for generating the ions derived from the target analyte ions.

[0024] By way of example, and without limitation, the ion interaction device can include a collision cell that is configured to cause dissociation of said target analyte ions to generate a plurality of product ions corresponding to the ions derived from the target analyte ions.

[0025] In a related aspect, a method of performing mass spectrometry is disclosed, which includes summing intensities associated with a plurality of datapoints in a chromatogram of at least one ion generated from a target analyte to generate at least one aggregate intensity value, where the intensities are summed within a window associated with an expected delivery time corresponding to delivery of the target analyte via a delivery device to an ion source of a mass spectrometer, comparing said aggregate intensity value with a limit of quantitation (LOQ) of the mass spectrometer for detecting said one or more ions generated from the target analyte, and indicating no signal peak is present within said window when the aggregate intensity value is less than the LOQ, e.g., less than the LOQ by at least 5% or 10% of the LOQ value.

[0026] In various embodiments, the above method can further include dividing the window into a plurality of portions each having a span (e.g., a time span) greater than an expected width of a peak signal associated with the at least one ion and summing intensities associated with datapoints within each of the window portions to compute a plurality of aggregate intensity values each corresponding to one of the window portions. This can be followed by identifying a maximum of the plurality of aggregate intensity values, comparing the maximum of the pluralityABS-0863 of aggregate intensity values with the LOQ, and identifying the window as not containing any signal peaks (i.e., as a “no peak” window) when the maximum is less than the LOQ.

[0027] Further understanding of various aspects of the present teachings can be obtained with reference to the following detailed description in conjunction with the associated drawings, which are described briefly below.Brief Description of the Drawings

[0028] FIG. 1 is a flow chart depicting various steps in an embodiment of a method according to the present teachings,

[0029] FIG. 2 is a flow chart depicting various steps for determining a default sample delivery time to be used in the method illustrated in the flow chart of FIG. 1,

[0030] FIG. 3 is a block diagram of a computer system configured to implement peak analysis methods according to various embodiments of the present teachings,

[0031] FIG. 4 schematically depicts a mass spectrometer according to an embodiment of the present teachings,

[0032] FIG. 5A illustrates an XIC chromatogram of hexythiazox- 1 , depicting integration of a wrong peak (i.e., the shaded peak), which is positioned in proximity of a correct large peak,

[0033] FIG. 5B shows another view of the XIC presented in FIG. 5A, which more clearly shows that the correct large peak was not integrated,

[0034] FIG. 5C is a plot of the integrated areas of XIC peaks associated with a plurality of standard samples containing different concentrations of hexythiazox- 1 as a function of the concentration of hexythiazox- 1 , where the data is fitted to a linear function,

[0035] FIG. 5D shows the integration of the correct peak from FIGS. 5A and 5B using a revised RT and RT window width based on standard samples shown in FIG. 5C that exhibit expected variation of peak areas as a function of the concentration of hexythiazox- 1 ,

[0036] FIG. 5E is another plot of the integrated areas of XIC peaks as a function of the concentration of hexythiazox- 1 fitted to a linear function, where the peaks were identified using a retention time computed as an average of retention times associated with samples that exhibitedABS-0863 expected variation of the peak areas as a function of the concentration of hexythiazox- 1 in the plot of FIG. 5D.Detailed Description

[0037] It will be appreciated that for clarity, the following discussion will explicate various aspects of embodiments of the applicant’s teachings, while omitting certain specific details wherever convenient or appropriate to do so. For example, discussion of like or analogous features in alternative embodiments may be somewhat abbreviated. Well-known ideas or concepts may also for brevity not be discussed in any great detail. The skilled person will recognize that some embodiments of the applicant’s teachings may not require certain of the specifically described details in every implementation, which are set forth herein only to provide a thorough understanding of the embodiments. Similarly, it will be apparent that the described embodiments may be susceptible to alteration or variation according to common general knowledge without departing from the scope of the disclosure. The following detailed description of embodiments is not to be regarded as limiting the scope of the applicant’s teachings in any manner.

[0038] As used herein, the terms "about" and "substantially equal" refer to variations in a numerical quantity that can occur, for example, through measuring or handling procedures in the real world; through inadvertent error in these procedures; through differences in the manufacture, source, or purity of compositions or reagents; and the like. Typically, the terms "about" and "substantially" as used herein mean 10% greater or less than the value or range of values stated or the complete condition or state. For instance, a concentration value of about 30% or substantially equal to 30% can mean a concentration between 27% and 33%. The terms also refer to variations that would be recognized by one skilled in the art as being equivalent so long as such variations do not encompass known values practiced by the prior art.

[0039] As used herein the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as

[0040] The term “an unknown sample,” as used herein, refers to a sample of interest having or being suspected of having an unknown quantity of a target analyte.ABS-0863

[0041] The terms “a standard sample” and “a calibration standard” are used herein interchangeably to refer to a well-characterized sample, e.g., a sample with a known composition, concentration of at least one calibrant analyte, that can be used to calibrate the operation of a mass spectrometer and / or the data generated by a mass spectrometer. By way of example, standard samples can be used to generate a calibration curve.

[0042] The term “internal standard,” as used herein, refers to a compound that can be added to a sample under analysis, a calibration standard and / or a blank sample and / or an unknown sample. By way of example, an internal standard can be a compound with similar chemical properties as those of the target analyte but distinguishable from the target analyte by a mass spectrometer. For example, the internal standard can be generated via isotopic labeling of the target analyte.

[0043] The term “peak,” as used herein, refers to an ion detection signal that exhibits an intensity variation as a function of a variable, e.g., as a function of time, such that the signal includes a maximum value. By way of example, in an extracted ion chromatogram (XIC), a peak represents an ion signal intensity as a function of time, e.g., an ion signal intensity corresponding to a target analyte as a function of time.

[0044] The term “average,” as used herein, is intended to indicate a value that can represent a set of measurement values. For example, the average can be a central or a typical value of a set of data. One example of an average of a set of data is an arithmetic mean of the dataset, i.e., the sum of the numerical values of the data points divided by the number of data points.Alternatively, the average value can refer to the middle value of a data set when the data points are ordered from the lowest value to the largest value. Further, in some cases, the average can correspond to the most frequent value in the dataset.

[0045] Without any loss of generality and for the ease of description, it is assumed in the following embodiments that the sample delivery device is an LC column. It should, however, be understood that the present teachings can be employed in connection with other types of sample delivery devices, e.g., a capillary electrophoresis device.

[0046] The present disclosure relates generally to methods and systems that provide an improved workflow for the quantitation of a target analyte in a sample under analysis. As noted above, in conventional methods, peak integrations in absolute quantitation workflows, which areABS-0863 typically performed by a software module, need to be reviewed by human users to ensure accuracy. However, such review of the peak integrations can be quite time consuming.

[0047] Many causes can contribute to potential inaccuracies of peak integrations. By way of example, the user’s specified expected RT and respective RT window may be somewhat “stale” such that peaks associated with a sample in a current batch may have at least partially shifted outside the specified RT window. Such loss of peak information may be avoided, for example, by using wider RT windows. However, the use of wider RT windows may result in integration of an interfering peak rather than the peak of interest (i.e., a peak corresponding to the target analyte).

[0048] In addition, RTs may drift as the acquisition of data for samples within a current batch is carried out. Such a drift would also call for wider RT windows, which would result in the same problem of potentially integrating an inferring peak rather than the peak of interest. Moreover, in conventional methods, noise below a spectrometer’s limit of quantitation (LOQ) is commonly identified as a “peak,” which would then require user intervention not to be reported as a valid peak.

[0049] In various embodiments, the present teachings can address the above problems by providing a step-based workflow approach for quantitation of a target analyte in an unknown sample. As discussed in more detail below, in various embodiments, the integration of a peak of an analyte of interest (i.e., a target analyte) across a set of standard samples containing different known concentrations of the target analyte, and optionally containing a known and substantially identical concentration of an internal standard (IS), can be used to inform the integration of the peaks associated with the target analyte in unknown samples.

[0050] With reference to the flow chart of FIG. 1, in one embodiment of a method according to the present teachings, a plurality of standard samples can be prepared, where each of the standard samples contains a known, but a different, concentration of a target analyte. Such standard samples can be generated using a variety of methods known in the art. By way of example, one method involves making successive dilutions of a starting mixture containing the highest concentration of the target analyte. A variety of matrices can be employed for preparation of the standard samples.ABS-0863

[0051] Each of the standard samples can be introduced into an LC column and the eluate exiting the LC column can be transferred to an ion source in order to ionize the target analyte in that sample to generate target analyte ions. The target analyte ions can be then introduced into a downstream mass spectrometer to generate ion detection data containing one or more ion detection peaks associated with the target analyte. By way of example, the one or more ion detection peaks can correspond to one or more peaks in an extracted-ion chromatogram (XIC) of the target analyte itself or of at least one product ion, such as a fragment ion formed via the dissociation of the target analyte. Alternatively, the one or more ion detection peaks can correspond to a mass peak in a mass spectrum of the target analyte or that of at least one product ion, e.g., a fragment ion, generated from the target analyte.

[0052] For each of the standard samples, at least one ion detection peak of the target analyte associated with an expected retention time (RT) corresponding to the passage of the target analyte through the LC column and within a default retention time window about the expected retention time is identified. For each of the samples, the identified ion detection peak is integrated to generate a respective integrated peak area. By way of example, in some embodiments, the width of the retention time window can be set based on previously-obtained data for an internal standard (IS), e.g., an IS added to the standard samples and as well as optionally the unknown samples, as discussed in more detail below. Alternatively, a default retention time window width can be set, e.g., based on the expected variation in the RT time associated with the target analyte.

[0053] The generated integrated peak areas can be utilized to obtain a calibration curve corresponding to the integrated peak areas associated with the different samples as a function of the concentration of the target analyte in those samples. Ideally, the relationship between the integrated peak areas and the concentration of the target analytes in the standard samples is linear (i.e., the integrated peak area is expected to increase linearly as the concentration of the target analyte increases). As such, in various embodiments, a linear relationship is used for fitting the peak area data as a function of the concentration of the target analyte to generate an initial calibration curve. By way of example, a least-squares regression method can be employed for fitting data points, each representing a peak area and a concentration of a target analyte in a respective standard sample, to a linear relationship.ABS-0863

[0054] The initial calibration curve can then be utilized to identify at least a subset of the standard samples (herein also referred to as “consistent samples”) that exhibit an expected variation of their integrated peak areas as a function of the concentration of the target analyte. It is noted that in some cases, all of the standard samples may exhibit an expected variation of their integrated peak areas as a function of the target analyte concentration.

[0055] By way of example, a set of consistent samples can be determined by identifying and eliminating one or more outlier samples from the set of the standard samples. In other words, one or more outlier standard samples can be identified and the remaining standard samples can be assumed to be consistent samples. By way of example, in some embodiments, those samples that lie within a margin of error associated with the calibration curve can be identified as those exhibiting an expected variation of their peak areas as a function of the concentration of the target analyte (i.e., consistent samples), while those samples that lie outside of the error margin (e.g., a sample exhibiting an integrated peak area that is a few standard deviations away from an integrated peak area predicted for that sample based on the calibration curve) can be identified as outliers.

[0056] While in some embodiments an initial calibration curve can be first generated as a best fit of the integrated peak areas as a function of the target analyte concentration to a curve fitting function and the outlier samples can then be identified as those that exhibit a significant deviation from the calibration curve, in other embodiments, the calibration curve can be constructed by inspecting each data point relative to previously fitted set of data points to determine whether the inspected data point should be used in the fit.

[0057] By way of example, in various embodiments, the initial calibration curve can be constructed without utilizing outlier data points. By way of example, the methods disclosed in Published International Application Number PCT / IB2016 / 050305 entitled “Automatic Quantitative Regression,” which is herein incorporated by reference in its entirety, can be used for generation of the calibration curve.

[0058] Briefly, the methods described in this published patent application can be used to generate the initial calibration curve by identifying an initial subset of data points, each of which corresponds to an integrated peak area associated with a standard sample and the concentration of the target analyte in that sample, that best fits a predefined curve fit type (here a linear fit),ABS-0863 e.g., based on curve fit metric, such R2. The subset of the data points includes data points at consecutive concentration levels of the target analyte in the standard samples. The number of data points in the initial subset is increased by testing whether the inclusion of one or more additional data points at the next immediate target analyte concentration level, either higher or lower, than the subset and any other data points previously tested would result in the generation of a suitable calibration curve using the curve fit type. The process can be repeated until all of the data points have been tested.

[0059] With continued reference to the flow chart of FIG. 1, the retention times corresponding to the consistent samples can be utilized to compute an average retention time and a respective standard deviation.

[0060] The computed average retention time can be used, respectively, as a revised RT relative to the expected RT. Further, a narrower revised RT window width can be set. For example, the revised RT window width can be based on the standard deviation of RTs associated with the consistent samples, e.g., it can correspond to a 95 or a 99 percent confidence interval.

[0061] In various embodiments, the average RT and the narrower RT window width can be utilized for integrating peaks associated with ion detection data acquired for one or more unknown samples.

[0062] In various embodiments, the ion detection data associated with the standard samples can be optionally re-analyzed using the revised RT and the revised RT window width. More specifically, for each of the standard samples, using the revised RT and the revised RT window width, the respective ion detection peak can be reintegrated to generate a revised set of peak areas for the standard samples.

[0063] The revised peak areas are then utilized to compute a revised calibration curve based on the integrated peak areas of the samples as a function of the concentration of the target analytes in those samples.

[0064] The revised calibration curve can then be utilized to determine a revised calibrated RT and a revised calibrated RT window width for use in integrating ion detection peaks associated with the target analyte in one or more unknown samples. By way of example, the revised calibration curve can be employed to identify a larger set of consistent standard samples, and theABS-0863 revised calibrated RT can correspond to an average of RTs corresponding to the expanded set of the consistent samples. In general, the calibrated RT window width is selected to be narrower than the default RT window width utilized for generating the initial calibration curve. By way of example, the calibrated RT window width can be based on the standard deviation of the revised RTs, e.g., a scaled version of the standard deviation corresponding to a 95 or a 99 percent confidence interval.

[0065] The revised calibrated RT together with the revised calibrated RT window width can then be employed to identify the at least one ion detection peak associated with the target analyte in ion detection data acquired from one or more unknown samples (e.g., samples in which the quantity of the target analyte is not known) and the identified ion detection peak can be integrated to determine its peak area.

[0066] In various embodiments, prior to the use of the standard samples to determine the calibrated RT and the calibrated RT window width, the ion detection data corresponding to one or more internal standards (IS’s) added to the standard samples (and also in some embodiments to the unknown samples) can be employed to determine the aforementioned expected RT and the default retention window width for the plurality of the standard samples.

[0067] By way of example, with reference to the flow chart of FIG. 2, in such an embodiment, each of the plurality of standard samples (and optionally each of the unknown samples) contains a known concentration of IS, where the concentration of the IS across the standard samples is substantially uniform. For example, the IS can be added to each of the standard samples (and optionally to the unknown samples). In various embodiments, the IS is different from the target analyte and its concentration is sufficiently high to allow its unambiguous detection in the standard samples.

[0068] For each of the standard samples containing the IS, at least one ion detection peak corresponding to the IS and associated with an expected RT time corresponding to the passage of the IS through an LC column (preferably the same LC column as that employed for the standard and the unknown samples) and associated with a default RT window width is integrated to generate a plurality of internal standard peak areas, each of which corresponds to one of the internal standard samples.ABS-0863

[0069] In general, the ion detection peaks associated with the internal standards can be reliably integrated since the concentrations of the internal standards are sufficiently high to generate an easily identifiable ion detection peak. However, in some cases, the integration of certain internal standard peaks may fail due to a variety of reasons. For example, the noise associated with an internal standard ion detection peak may lead to a partial integration of the peak or a peak may shift partially or completely outside the default retention window.

[0070] As such, in various embodiments, the standard samples having internal standards exhibiting peak areas and / or RTs that are significantly different from those of the other IS peaks associated with the other standard samples are identified and the retention times associated with the IS of the remaining standard samples (herein referred to as “confirmed IS standard samples”) are averaged to compute an average internal standard retention time (IS RT).

[0071] The average internal standard retention time can be compared with the expected internal standard retention time to determine a sample-specific IS RT shift. Without being limited to any particular theory, an RT shift may be due to a gradual drift of the RT across the samples. In various embodiments, the IS RT shifts corresponding to the different samples can be averaged to generate an average IS RT shift.

[0072] The average IS RT shift can then be utilized to derive an expected RT for the plurality of standard samples containing different concentrations of the target analyte. By way of example, the expected RT for the plurality of standard samples can be determined based on a previously-measured RT for the target analyte and the determined average IS RT shift. For example, the average IS RT shift can be utilized to provide a correction to the previously- measured target analyte RT. The derived expected RT can be utilized in a manner discussed above to generate a calibrated RT and a calibrated RT window width for use for analyzing one or more unknown samples expected to contain the target analyte.

[0073] It is possible in analysis of ion detection data for noise to be integrated as a legitimate peak. In various embodiments, a calibration curve generated according to the present teachings can be employed to provide automatic detection of such noise or otherwise valid peaks. In particular, if a target analyte concentration calculated for an integrated area of a peak based on the calibration curve is less than an expected or measured limit of quantitation (LOQ) of the mass spectrometer, the peak may be marked as “no peak” to indicate that it is likely that theABS-0863 presumed peak does not correspond to a valid peak. In various embodiments, such peaks may be nonetheless reviewed by a human operator to ensure that they in fact correspond to invalid peaks. Even with such human intervention, in many cases, a small subset of such peaks may be deemed as having been incorrectly classified as invalid peaks.

[0074] In various embodiments, the raw data may be examined to determine the maximum possible peak area that can be obtained. In some applications, such a peak area may result in a target analyte concentration less than the LOQ and hence can be marked as “no peak.” In various embodiments, such “no peak” determinations need not be reviewed manually since there is no possibility that even a different integration of the peak informed by a more accurate RT would result in a reportable peak area.

[0075] For example, consider a chromatogram with a large peak and a nearby much smaller one (or the smaller one may be just noise). Due to potential issues that may arise when using integration algorithms, the smaller peak (or noise) may be reported - one common reason is that such small ‘peak’ is closer to the (updated) expected RT than the larger one (even though the larger one is still within the expected window).

[0076] The area for this small peak may be less than the LOQ area, and hence it may be marked as ‘no peak.’ But in this example, the indication that no peak is present would not be correct as a different correct peak (i.e., the larger peak) is present within the RT window. So, the user needs to manually review all ‘no peak’ cases to make sure the ‘no peak’ designation is correct. Although this would be less time consuming than having to manually inspect all peaks, it is nonetheless a tedious task.

[0077] In various embodiments, to address the above problem, all the intensity values (e.g., total ion current in an XIC chromatogram) of datapoints in a window near the expected RT can be summed. Such summing of the intensity values (herein also referred to as y-values) does not require performing a peak-finding process. If this sum is small (compared to LOQ area), then there is great certainty (close to 100%) that there’s no real nearby peak and hence the user can safely omit reviewing such “no peak” designations. However, if the sum is larger than LOQ, a manual review by the user can still be recommended.ABS-0863

[0078] It is noted that in some cases, such summing of all data points in the window may result in a value greater than LOQ, but due to summing of noise and not one or more peak signals. Thus, in some embodiments, the window is divided into a plurality of portions, e.g., each containing ‘n’ sequential data points, and the sum of the intensities of the data points in each portion is computed. By way of example, ‘n’ is chosen such that ‘n’ datapoints would span a region that is somewhat wider than the width of a typical peak. The maximum value of the sum across the various portions can be considered as providing an upper bound on a possible peak area. In various embodiments, if such a maximum value is less than LOQ, no manual inspection of “no peak” designation would be required. In such embodiments, conventional peak-finding algorithms can be replaced with the above method of obtaining one or more sums of the intensity of the datapoints.

[0079] In various embodiments, a computer system can be employed to implement the present teachings for the integration of ion detection peaks, and in particular, for determining calibrated sample delivery time, e.g., a calibrated retention time (RT), and optionally a calibrated sample delivery time window width (e.g., an RT window width), which can enhance the integration of the ion detection peaks.

[0080] By way of example, FIG. 3 is a block diagram that illustrates such a computer system 100, upon which embodiments of the present teachings may be implemented. Computer system 100 includes a bus 102 or other communication mechanism for communicating information, and a processor 104 coupled with bus 102 for processing information. Computer system 100 also includes a memory 106, which can be a random access memory (RAM) or other dynamic storage device, coupled to bus 102 for determining base calls, and instructions to be executed by processor 104. Memory 106 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104. Computer system 100 further includes a read only memory (ROM) 108 or other static storage device coupled to bus 102 for storing static information and instructions for processor 104. A storage device 110, such as a magnetic disk or optical disk, is provided and coupled to bus 102 for storing information and instructions.

[0081] Computer system 100 may be coupled via bus 102 to a display 112, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user.ABS-0863An input device 114, including alphanumeric and other keys, is coupled to bus 102 for communicating information and command selections to processor 104. Another type of user input device is cursor control 116, such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor 104 and for controlling cursor movement on display 112. This input device typically has two degrees of freedom in two axes, a first axis (i.e., x) and a second axis (i.e., y), that allows the device to specify positions in a plane.

[0082] Consistent with certain implementations of the present teachings, results are provided by computer system 100 in response to processor 104 executing one or more sequences of one or more instructions contained in memory 106. Such instructions may be read into memory 106 from another computer-readable medium, such as storage device 110.Execution of the sequences of instructions contained in memory 106 causes processor 104 to perform the process described herein. Alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to implement the present teachings. Thus, implementations of the present teachings are not limited to any specific combination of hardware circuitry and software.

[0083] The term “computer-readable medium” as used herein refers to any media that can participate in providing instructions to processor 104 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media can include, for example, optical or magnetic disks, such as storage device 110. Volatile media can include dynamic memory, such as memory 106. Transmission media can include coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 102.

[0084] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other tangible medium from which a computer can read.

[0085] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor 104 for execution. For example, theABS-0863 instructions may initially be carried on the magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 100 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector coupled to bus 102 can receive the data carried in the infra-red signal and place the data on bus 102. Bus 102 carries the data to memory 106, from which processor 104 retrieves and executes the instructions. The instructions received by memory 106 may optionally be stored on storage device 110 either before or after execution by processor 104.

[0086] In accordance with various embodiments, instructions configured to be executed by a processor to perform a method according to the present teachings are stored on a computer- readable medium. The computer-readable medium can be a device that stores digital information. For example, a computer-readable medium includes a compact disc read-only memory (CD- ROM) as is known in the art for storing software. The computer-readable medium is accessed by a processor suitable for executing instructions configured to be executed.

[0087] The methods according to the present teachings for performing mass spectrometry can be incorporated in the data analysis modules of a variety of mass spectrometers.

[0088] By way of example, FIG. 4 schematically depicts a mass spectrometer 400 according to an embodiment that includes an LC column 402 that can receive a sample and separate a plurality of analytes in the sample based on their elution times from the LC column. The mass spectrometer 400 further includes an ion source 404 that receives an eluate exiting the LC column and ionizes one or more analytes, e.g., a target analyte to be quantified, contained in the eluate to generate a plurality of precursor ions, e.g., target analyte ions.

[0089] An RF voltage source 410 and a DC voltage source 412, operating under control of a controller 418, apply RF and DC voltages, respectively, to the rods of the mass filter 408 so as to configure the mass filter to provide a desired ion transmission window.

[0090] By way of example, in some embodiments, in use, the controller 418 can set the ion transmission window of the mass filter to allow the passage of a target analyte therethrough. By way of example, the mass filter 408 can include a plurality of rods arranged in a quadrupoleABS-0863 configuration to which RF voltages as well as a discriminating DC voltage can be applied via the RF and the DC voltage sources, respectively, to generate the ion transmission window.

[0091] In this implementation, the ions passing through the mass filter 408 are received by an ion fragmentation device 414 that can cause fragmentation of the precursor ions to generate a plurality of product ions. The product ions are received by a mass analyzer 416, which generates mass signal data associated with the product ions.

[0092] In other embodiments, the mass spectrometer 400 may not include the ion fragmentation device 414 or may not, in some data acquisition modes, utilize the ion fragmentation device 414 for dissociating the target analyte ions.

[0093] In other words, the mass spectrometer 400 can operate in a variety of different modes to generate different types of ion detection data. By way of example, in some embodiments, the ion detection data generated by the mass analyzer 416 can correspond to an XIC of the target analyte. In another data acquisition mode, the ion detection data generated by the mass analyzer 416 can correspond to an m / z mass spectrum of the target analyte ions or the fragment ions generated via dissociation of the target analyte ions.

[0094] An analysis module 420 (herein also referred to as an analysis unit) that is in communication with the mass analyzer 416 and the controller 418, such as the computer system 100 discussed above, can receive the ion detection data generated by the mass analyzer 416 and use the methods according to the present teachings to process the ion detection data.

[0095] By way of example, prior to collecting ion detection data corresponding to a target analyte in an unknown sample, the mass spectrometer may be utilized to process a plurality of standard samples having different, but known, concentrations of the target analyte, to generate ion detection signals corresponding to the target analyte in those standard samples.

[0096] In various embodiments, the analysis module 420 can process the ion detection signals associated with the standard samples using embodiments of methods according to the present teachings. By way of example, as discussed above, the analysis module 420 can generate a calibration curve corresponding to the integrated areas of a peak associated with the target analyte in the standard samples as a function of the known concentrations of the target analyte in those samples. By way of example, the analysis module 420 can then derive a calibrated RT andABS-0863 a calibrated RT window width based on the RTs of a plurality of consistent standard samples identified based on the calibration curve as well as the respective standard deviation of the RTs for use in analysis of ion detection peaks of the target analyte in unknown samples.

[0097] The following example is provided for elucidating various aspects of the present teachings and is not provided to necessarily indicate an optimal way of practicing the present teachings and / or optimal results that can be obtained.

[0098] Example

[0099] By way of example, FIG. 5A illustrates an XIC chromatogram generated by an LC- MS mass spectrometric system for a target analyte; in this case, hexythiazox- 1. According to conventional methods, an algorithm can be utilized to identify one or more peaks associated with the target analyte to determine its concentration. In this example, an expected retention time (RT) of 13.75 minutes is used for identifying the peak(s) of interest. Using such an RT would lead to the incorrect identification of the shaded peak as the peak of interest, which would in turn lead to an incorrect estimation of the concentration of the target analyte, as the shaded peak in this example is merely noise associated with the measurement process. By way of further illustration, FIG. 5B is another view of the XIC in FIG. 5A, which more clearly shows that the correct large peak was not integrated.

[0100] In contrast, the processing of the XIC chromatogram using an embodiment of the present teachings can lead to the correct identification of the peak of interest by updating the RT based on measurements performed on a plurality of standard samples. Thus, in one example of a method according to the present teachings, a plurality of standard samples can be prepared, where each standard sample contains a known, but different, concentration of the target analyte, in this case hexythiazox- 1 , as noted above. The XIC chromatograms of such standard samples can then be obtained. .

[0101] It is noted that in general various concentrations of the target analyte in the standard samples can be selected such that the peak associated with the target analyte in a respective chromatogram can be readily identified.

[0102] The peak areas as a function of the concentration of the target analyte can be plotted and fitted to a linear function, e.g., using a least-squares regression method, as shown in FIG.ABS-08635C. The datapoints corresponding to the outlier samples can be identified (e.g., datapoints depicted as unfilled circles), and the retention times corresponding to the other samples, i.e., the consistent samples, can be used to compute an average retention time (RT) and an associated standard deviation.

[0103] In various embodiments, the average RT can be used as the initial expected RT for analysis of one or more unknown samples. Further, in various embodiments, an initial RT window width for the analysis of the one or more unknown samples can be set based on the computed standard deviation, e.g., by scaling the standard deviation to obtain a 95% or a 99% confidence interval.

[0104] FIG. 5D shows the integration of the correct peak from FIGS. 5A and 5B using a revised RT and RT window width calculated based on the consistent samples identified in FIG. 5C.

[0105] In various embodiments, the standard samples can be reanalyzed using the average RT and an RT window width set based on the computed standard deviation. More specifically, the peak areas corresponding to a target analyte in the standard samples can be reintegrated using the average RT and the revised RT window width. The newly computed peak areas can then be plotted relative to the target analyte concentration associated with the standard samples and fitted to a linear function, as shown in FIG. 5E. The fit illustrated in FIG. 5E demonstrates that employing the revised RT can lead to reclassification of certain datapoints. Specifically, some datapoints that were considered previously as outlier datapoints are now aligned well with the linear fit. In other words, the use of the revised RT has expanded the set of consistent standard samples. In various embodiments, the RTs associated with the expanded set of standard samples can be averaged to arrive at a new calibrated RT to be used as the expected RT for analysis of peaks associated with one or more unknown samples. Further, a new RT window width can be set based on the associated standard deviation of the RTs of the expanded set of consistent samples.

[0106] The above descriptions of various implementations of the present teachings have been presented for purposes of illustration and description. It is not exhaustive and does not limit the present teachings to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the present teachings. Additionally,ABS-0863 the described implementation includes software but the present teachings may be implemented as a combination of hardware and software or in hardware alone. The present teachings may be implemented with both object-oriented and non-object-oriented programming systems.

[0107] Depending on certain implementation requirements, embodiments of the present teachings, the controller can be implemented in hardware, firmware and / or in software.

[0108] In some embodiments, the instructions for operating the optical system can be stored using a non-transitory storage medium such as a digital storage medium, for example a DVD, a Blu-Ray, a CD, a ROM, a PROM, and EPROM, an EEPROM or a FLASH memory, having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed. Therefore, the digital storage medium may be computer readable.

[0109] While various embodiments have been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive; embodiments of the present disclosure are not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing embodiments of the present disclosure, from a study of the drawings, the disclosure, and the appended claims.

[0110] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other processing unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measured cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0111] Those having ordinary skill in the art will appreciate that various changes can be made to the above embodiments without departing from the scope of the present teachings.

Claims

ABS-0863What is claimed is:

1. A method of performing mass spectrometry, comprising: for each of a plurality of standard samples containing different concentrations of a target analyte, integrating at least one ion detection peak associated with said target analyte over a time window defined by an expected delivery time and an initial delivery time window width corresponding to delivery of said target analyte via a delivery device to an ion source of a mass spectrometer so as to obtain an integrated peak area for each of the plurality of standard samples, generating a calibration curve corresponding to said integrated peak areas as a function of the concentration of the target analyte in said standard samples, identifying at least two of said plurality of standard samples that exhibit an expected variation of their integrated peak areas as a function of the target analyte concentration, computing an average delivery time and a measured variation of the delivery time based on the delivery times associated with the identified standard samples, and determining a calibrated delivery time and a calibrated delivery time window width based on said average delivery time and said measured variation of the delivery time for use in integrating one or more ion detection peaks associated with said target analyte in one or more unknown samples.

2. The method of Claim 1 , wherein said calibrated delivery time is substantially equal to said average delivery time, or wherein said calibrated delivery time window width corresponds to a confidence interval in a range of about 90 to about 99 percent around said calibrated delivery time.

3. The method of Claim 1 or Claim 2, wherein said delivery device comprises any of a liquid chromatography (LC) column, a capillary electrophoresis device, and a gas chromatography device, wherein, optionally, said capillary electrophoresis device is configured to employ imaged capillary isoelectric focusing (icIEF), andABS-0863 wherein, optionally, the delivery time associated with said LC column is substantially equal to a retention time (RT) of said target analyte as it passes through the LC column.

4. The method of Claim 1 or Claim 2, further comprising reintegrating the ion detection peaks associated with said target analyte in said standard samples using the average delivery time and a delivery time window width defined based on said measured variation of the delivery time to generate a revised calibration curve.

5. The method of Claim 4, further comprising utilizing said revised calibration curve to determine a revised calibrated delivery time and a revised calibrated delivery time window width for use in integrating one or more ion detection peaks associated with said target analyte in said one or more unknown samples, and optionally, utilizing said revised calibrated delivery time and said revised calibrated delivery time window width to integrate at least one ion detection peak associated with said target analyte in at least one of said one or more unknown samples.

6. The method of Claim 1, further comprising utilizing said calibrated delivery time and said calibrated delivery time window width to integrate at least one ion detection peak associated with said target analyte in at least one of said one or more unknown samples.

7. The method of Claim 1, wherein the step of identifying said at least two standard samples exhibiting expected variation of integrated areas as a function of concentration of the target analyte comprises identifying one or more of the standard samples exhibiting a concentration of the target analyte that is within an error margin of the calibration curve.

8. The method of Claim 7, wherein said error margin of the calibration curves is defined based on a standard deviation associated with fitting the integrated peak areas of the standard samples as a function of the concentration of the target analyte to a predefined curve fitting function, and wherein, optionally, said predefined curve fitting function is a linear function of the integrated peak areas versus the concentration of the target analyte.ABS-08639. The method of Claim 1, wherein each of said standard samples comprises a substantially identical, and known concentration of one or more internal standards (IS), and the method further comprises: for each of the standard samples, integrating at least one ion detection peak corresponding to said IS and associated with an expected IS delivery time and a default IS delivery time window width to generate a plurality of IS integrated peak areas, using the plurality of IS integrated peak areas to identify at least two of said plurality of standard samples exhibiting expected IS integrated peak areas, determining an average IS delivery time based on the delivery times associated with said at least two identified standard samples, determining a delivery time shift by comparing said average IS delivery time with the expected IS delivery time, and utilizing said delivery time shift to define said expected delivery time of the target analyte for the standard samples.

10. The method of Claim 1, wherein each of the standard samples and the one or more unknown samples comprises a substantially identical, and known concentration of the IS, and the method further comprises: for each of the standard and the unknown samples, integrating at least one ion detection peak corresponding to said IS and associated with an expected IS delivery time and a default IS delivery time window width to generate a plurality of IS integrated peak areas, using the plurality of IS integrated peak areas to identify at least two of said plurality of the standard and the unknown samples exhibiting expected IS integrated peak areas, determining an average IS delivery time based on the delivery times associated with said at least two identified samples,ABS-0863 determining a delivery time shift by comparing said average IS delivery time with the expected IS delivery time, and utilizing said delivery time shift to define said expected delivery time of the target analyte for the standard samples.

11. The method of Claim 9, further comprising setting said initial delivery time window width for the standard samples to be narrower than said default IS delivery time window width.

12. The method of Claim 9, wherein the IS is chemically different than the target analyte.

13. The method of Claim 9, wherein the known concentration of the IS is sufficiently high to allow an unambiguous identification of said at least one ion detection peak of the IS.

14. A mass spectrometer, comprising: an ion source for ionizing a sample containing a target analyte to generate a plurality of ions corresponding to said target analyte, a sample delivery device for delivering the sample to said ion source, a mass analyzer for generating at least one ion detection signal associated with any of said target analyte ions or ions derived from said target analyte ions, wherein said at least one ion detection signal comprises at least one ion detection peak, a data analysis module configured to: receive ion detection signals corresponding to a plurality of standard samples containing different concentrations of the target analyte, each of the ion detection signals including at least one ion detection peak associated with the target analyte, for each of the standard samples, integrate the respective at least one ion detection peak over a time window associated with an expected delivery time and an initial delivery time window width corresponding to delivery of said target analyte via said delivery device to the ion source so as to obtain an integrated peak area for each of the plurality of standard samples,ABS-0863 generate a calibration curve corresponding to said integrated peak areas as a function of the concentration of the target analyte in said standard samples, identify at least two of said standard samples exhibiting an expected variation of their respective peak areas as a function of the target analyte concentration, compute an average delivery time and a measured variation of the delivery time based on the delivery times associated with the identified standard samples, and determine a calibrated delivery time and a calibrated delivery time window width based on said average delivery time and said measured variation of the delivery time for use in integrating the at least one ion detection peak associated with the target analyte.

15. The mass spectrometer of Claim 14, wherein said sample delivery device comprises any of an LC column, a capillary electrophoresis device, and a gas chromatography device.

16. The mass spectrometer of Claim 14, wherein said at least one ion detection signal generated by the mass analyzer comprises an extracted ion chromatogram (XIC).

17. The mass spectrometer of any one of Claims 14 - 16, further comprising a mass filter positioned between the ion source and the mass analyzer, wherein said mass filter is configured to allow passage of said target analyte ions therethrough.

18. The mass spectrometer of Claim 17, further comprising an ion interaction device positioned between the ion source and the mass filter for generating said ions derived from the target analyte ions, wherein, optionally, said ion interaction device comprises a collision cell configured to cause dissociation of said target analyte ions to generate a plurality of product ions corresponding to said ions derived from said target analyte ions.

19. A method of performing mass spectrometry, comprising: summing intensities associated with a plurality of datapoints in a chromatogram of at least one ion formed from a target analyte to generate at least one aggregate intensity value, where the intensities are summed within a window associated with an expected delivery timeABS-0863 corresponding to delivery of the target analyte via a delivery device to an ion source of a mass spectrometer, comparing said aggregate intensity value with a limit of quantitation (LOQ) of the mass spectrometer for detecting said at least one ion, and indicating no signal peak is present within said window when the aggregate intensity value is less than the LOQ.

20. The method of Claim 19, further comprising dividing said window into a plurality of portions each having a span greater than an expected width of a peak signal associated with said at least one ion and summing intensities associated with datapoints within each of said window portions to compute a plurality of aggregate values each corresponding to one of said window portions, and, optionally, further comprising: identifying a maximum of said plurality of aggregate values, comparing said maximum of the plurality of aggregate values with the LOQ, and identifying said window as not containing any signal peaks when said maximum is less than said LOQ

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