Patent ID: 12217952

DETAILED DESCRIPTION

A method of mass spectrometry is disclosed and may begin with the step of providing a library of matrix data. The matrix data comprises one or more physico-chemical properties of one or more matrix components as a function of retention time, such as the mass to charge ratios of a number of matrix peaks. A sample, which could be taken from a number of samples having the same origin (e.g., urine samples from a plurality of people, soil samples from a particular area), is chromatographically separated. The sample contains at least some of said matrix components and one or more analyte components. For example, the sample may be urine and the matrix component(s) may be urea, uric acid, etc., while the analyte component(s) may be traces of certain drugs.

The sample is analysed at a plurality of retention times to obtain sample data, wherein the sample data comprises one or more physico-chemical properties of one or more sample components as a function of retention time. The sample components may comprise or correspond to one or more matrix components, for example the same matrix components that are used to provide the library of matrix data.

One or more error values may be calculated as a function of retention time based on a comparison between said sample data and said matrix data. The comparison may comprise comparing a physico-chemical property of the one or more matrix components in the sample data with the corresponding physico-chemical property of the same matrix components in the matrix data, at one or more retention times. The one or more error values may correspond to the difference between the physico-chemical property of the matrix component in the sample data and the physico-chemical property of the matrix component in the matrix data, at the one or more retention times.

Matrix components or compounds may be defined as the components of a mixture other than analyte(s). For a given origin of sample the presence of common or known matrix ions may be highly predictable. The matrix may be a biological matrix, for example plasma, urine, faeces or bile.

A matrix may be known before analysis begins and in many cases matrix matched standards have been prepared. A matrix, for example plasma, urine, faeces, bile, soil or a particular food may contain many endogenous compounds which will give rise to many highly reproducible chromatographic peaks over the retention time range in which analytes of interest elute. The composition of each type of matrix is substantially constant regardless of the origin of the sample. Various embodiments take advantage of this by using the endogenous compounds of the matrix, which elute at various retention times in an analysis run, to correct for errors in a sample containing such compounds as a function of retention time.

It should be appreciated that the composition of the matrix may be known beforehand, in the sense that it has a standard chromatographic elution profile that can be provided by reference to a known library.

However, the library of matrix data, including the physico-chemical properties of the matrix components, can alternatively be provided by chromatographically separating a sample containing the matrix components in an initial run, prior to the analytical run of the sample, so as to determine the physico-chemical properties of the matrix components as a function of retention time.

In this manner, it is not necessary that the matrix itself, or the matrix components can be identified, as long as the library of matrix data includes physico-chemical properties as a function of retention time. For example, the matrix and/or the matrix components may be unknown throughout the analysis. What is important is how the physico-chemical property of the matrix (whatever it may be) changes throughout the analytical run, as this is what is used to calculate the error values.

The approach according to various embodiments optionally removes the requirement for an internal or external lock mass or ion mobility lock drift to be provided and also optionally reduces experimental and instrument complexity.

FIG.1shows a base peak chromatogram of a sample of human urine acquired by liquid chromatography-Time of Flight mass spectrometry. The chromatogram is dominated by many intense peaks which give rise to mass spectra at each retention time. Many of these peaks relate to matrix ions and are of little or no analytical interest.

These matrix ions may be in different concentrations in samples from different species or individuals but a large enough proportion or subset of these compounds will be present in sufficient concentration in every sample of urine such that they may be regarded as characteristic of this matrix.

It should be noted that it may not be necessary to fully characterize these matrix ions in terms of elucidation of elemental composition, exact mass etc. in order to use these matrix ions to perform internal calibration of all the mass spectral peaks in the analysis.

Firstly, a plurality of samples may be provided and the chromatographic profile and/or mass, mass to charge ratio or ion mobility spectra of the plurality of samples may be recorded in an initial or non-analytical run. The system may have been calibrated with reference standards prior to analysis. The samples may comprise analyte components of interest dispersed in a matrix, wherein the matrix may be common to all of the samples and may contain endogenous matrix components or compounds.

For example, the samples may be a plurality of samples of urine. Amongst other things, the common endogenous compounds found in a urine matrix may include urea, creatinine, uric acid, citrate, host/pathogen DNA, host/pathogen RNA, amino acids, immunoglobulin, tamm-horsfall protein, albumin and many more compounds. These compounds may be common to all of the samples, and may have the same physico-chemical properties regardless of the particular sample taken.

Alternatively, the samples may be taken from one or more apples, in which case the matrix would be e.g. endogenous sugars in the apple and the method may comprise detecting levels of a particular analyte, for example a pesticide. The samples may all contain common, endogenous matrix components that have the same physico-chemical properties regardless of the particular sample taken. These could be used as the matrix components of the method disclosed herein.

Secondly, the data may be processed to produce a library of components and a determination may be made regarding which components are common to the matrix, regardless of its origin. A library of mass, mass to charge ratio, ion mobility, differential ion mobility, drift time, collision cross section (“CCS”), interaction cross section and/or retention time may be constructed for the matrix components.

A two dimensional peak detection algorithm such as APEX3D or 4D may be used to reduce each chromatographic feature to mass, mass to charge ratio, ion mobility, drift time, collision cross section (“CCS”), interaction cross section and/or retention time.

A sample may be run several times, or different samples may be run and analysed to improve the confidence in the library entries for the matrix components.

Certain samples may be well-known and the first and second steps provided above may not be required. For example, a library of matrix data comprising one or more physico-chemical properties of one or more matrix components as a function of retention time may be provided by reference to a known database.

Alternatively, for matrix peaks with unknown composition the expected physico-chemical property values associated within the library may be initially determined using conventional internal or external lock mass approaches known in the prior art.

The expected mass to charge ratio values for matrix peaks with known elemental composition may be calculated directly. Similarly, if the ion mobility, collision cross section (“CCS”) or interaction cross section value for a matrix ion is known this may be recorded in the library directly. Alternatively, internal or external lock mobility approaches may be used to ensure accurate library entries.

Care must be taken to avoid matrix peaks which may have mass interference or are statistically imprecise. The presence of possible interference may be examined for example by comparison of peak shape or width to a model or expected peak shape or width. If a peak contains interference it may be rejected and not added to the library.

The statistical precision of mass and/or mobility measurement may be recorded with each measurement and optionally used to weight the contribution of particular signals to the final calibration applied.

Thirdly, further samples may then be analysed in subsequent or analytical runs. The data is optionally post processed to locate and record the physico-chemical properties (e.g., mass to charge ratio values) of the matrix peaks in the library which may be matched to corresponding matrix peaks in the subsequent or analytical samples. As many matrix peaks as possible should optionally be located. Signals which are very weak or which have mass interference are optionally avoided. Not all peaks in the library may be located or used for a particular analysis. Peaks may be located using one or more of mass, mass to charge ratio, ion mobility, differential ion mobility, drift time, collision cross section (“CCS”), interaction cross section or retention time.

Fourthly, one or more error values as a function of retention time may be calculated based on a comparison between the physico-chemical properties of the one or more matrix components in the sample data with the corresponding physico-chemical properties of the same matrix components in the matrix data, at one or more retention times.

For example, a graph may be created or otherwise calculated that plots (or calculates) error values, for example mass or mass to charge ratio error values, as a function of retention time for the matrix ions in one or more of the mass spectra created in the subsequent or analytical runs. In the case of ion mobility spectrometry, for example, the error values may be a percentage error of collision cross section. Most spectra will contain at least one matrix ion matched to the library. The errors calculated for each matched peak at each retention time may be averaged to a single value and assigned with an appropriate statistical error or processed separately.

Fifthly, an error function may be produced from the error values as a function of retention time. For example, a line of fit may be plotted through the graph or otherwise calculated, or a function corresponding to a line of fit may be calculated. Outliers may be ignored and statistical precision may be accounted for. The maximum curvature may be limited based on the gradual short term drift expected from the system. Methods of determining a best fit to such data and recognising outliers are known.

It will be appreciated that the error values being plotted as a function of retention time is due to retention time being the timescale of chromatographic separation.

FIG.2shows a representation of a line of fit1for a plot of error as a function of retention time. The illustrated line of fit1represents the mass to charge ratio error in parts-per-million (ppm) of the peaks in the sample which match entries in the library as a function of retention time. The line of fit1may be a line of best fit.

Three outliers2are shown which have been determined not to fit the data trend and so have been excluded from the calculation of the line of fit1. The outliers2may fall off the general trend line due to interference or mass assignment. The shape of the curve optionally represents the way the mass assignment has drifted during the course of the experimental retention time. This is known as mass to charge ratio drift and can occur, for example, due to the effect of ambient temperature changes during the experimental time. For a given system the maximum rate of change of mass to charge ratio, and hence the maximum curvature of this line, may be known or calculated. This may be used to restrain the curve and as a basis to reject outliers.

Fitting a smooth curve through all of the data may inherently smooth out statistical variations and closely model the expected behaviour of the system. It may also allow a region3of the chromatogram where no or few matrix ions are matched to the library to be corrected, based, for example, on the general trends observed before and or after these regions.

Alternatively, each spectrum or an averaged region of spectra may be corrected independently. Other methods of processing error (e.g., ppm) versus time data may be used such as calculating a moving average of the error with time.

Sixthly, the mass to charge ratio of one or more analyte peaks at their respective retention times may be corrected based on the error function, for example using the line of best fit to calculate an error value at the retention time of the analyte peak.

If MSEor scheduled or data dependent MS-MS experiments are performed, the library may contain precursor and fragment ions from the matrix. This can add to the number of peaks at each retention time which may be used for mass correction, and may improve the confidence of assignment of matrix ions in the sample to the entries in the library. Correction values calculated from the MS and MS-MS data at each retention time may be pooled or averaged to improve the accuracy.

In another embodiment correction may be calculated and applied during the chromatographic elution in real time, optionally based on identified matrix peaks. For example, real time correction of a current spectrum may use a moving average of correction values, optionally calculated from identified matrix ions in a number of previously acquired spectra.

Other instrument parameters may be monitored (e.g. mass resolution) in order to monitor instrument performance or apply real time correction or tuning adjustment.

Using real time monitoring of matrix ion mass to charge ratio values it is then possible to identify mass shifts caused by detector saturation in Time of Flight mass spectrometers, space charge aberrations in analytical RF or electrostatic ion traps or drift time shifts in ion mobility spectrometer or separator devices. This information may be used in real time to adjust instrument parameters e.g. one or more of ion transmission, ionisation efficiency, detector gain or ion trap fill time in order to compensate for these effects.

Alternatively, this information may be used in post processing to determine a correction to be applied to an analyte signal in order to compensate for shifts or other aberrations due e.g. to space charge effects at a particular retention time.

Mobility may be included and the disclosed methods may be used to correct for mobility drift or lock drift. The collision cross section (“CCS”), interaction cross section or drift time may be used as confirmation of the identity of a matrix peak to improve the library assignment.

In the case of proteomics many samples may be known to contain commonly found proteins such as keratin or ubiquitin. Peptides from these proteins may be used to correct drift during the separation. In the case of proteomics the database may be a protein/peptide database containing precursor and fragment ions.

In embodiments a known matrix or calibrant may be added, spiked or otherwise introduced into a sample of analyte such that known chromatographic peaks with known mass to charge ratios appear within the final data. The added matrix or calibrant mixture may be designed such that it does not elute at the same time as the analyte of interest, and therefore may not give rise to ionization suppression effects, interference or mass interference. In addition, the added matrix or calibrant may be designed such that it is separated from the analyte in mass to charge ratio or drift time space, optionally reducing the possibility of mass interference.

For example, when analysing protein digests a known digest of a protein or other type of mixture of compounds may be spiked into the sample to act as a lock mass during the chromatographic elution of the analyte peptides.

In small molecule quantification C13 labelled isotopes are often used with the analyte as internal quantification standards. These have the same retention time as the analyte but different known mass to charge ratio value. These may be used to correct for mass drift during the chromatographic elution.

Multi point lock mass may also be performed. In this case higher than first order lock mass correction may be applied. This may be used to correct for time offset drift from electronic timing circuits in time of flight systems. In the limit a mass calibration curve may be constructed for each retention time or retention time range, optionally by the method described and applied to the data.

According to an embodiment the method may be used in combination with a standard internal or external lock mass e.g. as a quality control check to make sure that the instrument calibration and or lock mass is correct.

Alternatively, the method may be used in combination with an external lock mass so that the drift in matrix ion mass to charge ratio may be monitored and used for internal correction and optionally to determine when to introduce an external lock mass during the chromatographic run. For example, correction up to 5 ppm may be allowed using the method described. Once the drift is determined to be outside this value then an external lock mass correction may be performed. This may reduce the number of external lock mass events to a minimum and may allow the frequency of lock mass introduction to be matched to the environmental conditions, optionally via the measured drift in the mass to charge ratio of the matrix ions.

Various embodiments may also remove the requirement for an internal or external lock mass or ion mobility lock drift, reducing experimental and instrument complexity.

The method may be applied to ion imaging, for example Matrix Assisted Laser Desorption/Ionisation (“MALDI”) or Desorption Electrospray Ionisation (“DESI”) tissue imaging, optionally using an orthogonal acceleration Time of Flight instrument.

In many imaging experiments, for example MALDI or DESI imaging, the origin of sample to be imaged may be well known. For example, the sample may be liver, muscle or other tissue from a known species. A library containing one or more physico-chemical properties of matrix components may be produced, wherein the matrix components correspond to common, reproducible compounds found in the sample.

Many matrix compounds or components may be found, which compounds or components may be substantially common over specific regions of, or over the whole surface of a particular sample. These compounds or components may be used to build an accurate library of matrix data to use in the disclosed method. In the case of animal tissue, for example, the matrix ions may arise from lipids, small proteins, or peptides.

The method may include providing a library of matrix data, the matrix data comprising one or more physico-chemical properties of one or more matrix components. The library containing the physico-chemical properties of one or more matrix compounds or components may be produced by analysing several tissue samples of similar type. This library may contain no spatial information.

The method may include imaging a sample at a plurality of spatial locations, the sample containing at least some of said matrix components and one or more analyte components. The method may include analysing said sample at said plurality of spatial locations to obtain sample data, said sample data comprising one or more physico-chemical properties of one or more sample components (including at least some of said matrix components).

When the sample is imaged in one or more analytical runs, an array of mass spectra, each associated with a given spatial location or range of spatial locations, may be produced. The time or time period at which each mass spectrum is produced may also be recorded along with the spatial information.

One or more matrix compounds or components may be identified in the mass spectrum or spectra obtained during the analytical run(s). One or more physico-chemical properties of the matrix compound(s) or component(s) in the sample data may be recorded or determined. The physico-chemical properties may include one or more of mass to charge ratio, drift time, collision cross section (“CCS”), interaction cross section, ion mobility and differential ion mobility.

Error values may be determined or calculated by comparing the physico-chemical properties of the matrix components in the library (i.e. the matrix data) to the physico-chemical properties of the same matrix components in the (analytical) sample data. A time value or time period may be associated with each error value by determining the time or times at which the mass spectra or mass spectrum containing the respective matrix component was recorded. Thus, the error values can be recorded as a function of time, and hence associated with a given spatial location or range of spatial locations.

The analyte data (i.e. not corresponding to matrix) that was acquired at each spatial location can then be corrected based on the error values. For example, a plot of the error values as a function of time (and therefore spatial location) may be produced, and one or more adjustment or correction values may be determined from the plot. For example, a function corresponding to a line of best fit of the plot of error values may be determined, and the one or more adjustment or correction values may be determined from the function corresponding to the line of best fit.

The analyte data (e.g., mass spectra or spectrum) may therefore be corrected at each spatial location, or range of spatial locations. It will be appreciated that the data that is corrected may be mass to charge ratio values in the mass spectra or spectrum.

Any or all of the mass spectra obtained during the analytical run may be corrected or adjusted by determining the times at which, or time period within which, each mass spectrum was recorded, determining an adjustment or correction factor at each time or time period (using the function) and applying each adjustment or correction factor to the mass spectrum obtained at its respective time or time period.

In some cases the sample may be inhomogeneous. For example a section of an entire animal may be imaged. In this case optical imaging may be used to locate the spatial coordinates containing known general tissue types (liver, heart, brain etc.).

When the library of matrix data is constructed the matrix components detected may be associated with a particular region of the image corresponding to a known tissue type. This is analogous to associating a measured matrix value to a particular retention time in the embodiments described above.

In some cases certain matrix components will only be associated with particular regions of the sample. This information may be used to direct matching of matrix ions observed in subsequent (analytical) sample data. For example, the physico-chemical properties of matrix components in the library may be recorded with an associated location or sample region (e.g., liver, heart, brain, etc.).

When carrying out the analytical run, the identification of the matrix components or compounds in the mass spectra (i.e. sample data) may include restricting the matrix data to those matrix components or compounds that correspond to the same location or sample region as the mass spectra. This can reduce the possibility, for example, of the same matrix component being used but with an incorrect physico-chemical property.

The matrix components used in the comparison between the sample data and the matrix data may be restricted to those matrix components or compounds that correspond to the same location or sample region as the mass spectra.

This approach may be extended to other analysis employing imaging or other surface sampling techniques. It is known, for example, in MALDI that eluent from a chromatographic separation may be deposited spatially onto a target strip creating an image of the chromatographic separation. Again matrix ions may be used to apply drift correction by the methods described above.

Samples for MALDI or DESI etc. may be individually deposited in specific locations on a target plate and subsequently analysed. Again the time at which each location of the target plate is analysed may be recorded as described above, allowing drift to be corrected at each target plate location using correlation between the matrix library and the sample.

Many other surface analysis or ion imaging techniques are known. For example, the above-described methods may be used in Direct Analysis in Real Time (“DART”), Matrix Assisted Inlet Ionisation (“MAIV”), Liquid Micro-Junction Surface Sampling (“LJM-SSP”), Liquid Extraction Surface Analysis (“LESA”), Low Temperature Plasma (“LTP”), Flowing Atmospheric Pressure Afterglow (“FAPA”), or Laser Ablation Electrospray Ionisation (“LAESI”).

Although the present disclosure has been described with reference to various embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the scope of the disclosure as set forth in the accompanying claims.