EP2460175B1 - Traitement de données d'analyse spectrale - Google Patents

Traitement de données d'analyse spectrale Download PDF

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Publication number
EP2460175B1
EP2460175B1 EP10739968.5A EP10739968A EP2460175B1 EP 2460175 B1 EP2460175 B1 EP 2460175B1 EP 10739968 A EP10739968 A EP 10739968A EP 2460175 B1 EP2460175 B1 EP 2460175B1
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data
value
sample
substance
data points
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German (de)
English (en)
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EP2460175A1 (fr
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Michael Paul Alfred May
Matthew James Kelly
Lkuo Konishi
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Shimadzu Corp
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Shimadzu Corp
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J49/00—Particle spectrometers or separator tubes
    • H01J49/0027—Methods for using particle spectrometers
    • H01J49/0036—Step by step routines describing the handling of the data generated during a measurement

Definitions

  • the present invention relates to methods and apparatus suitable for analysing spectra. More specifically, but not exclusively, the present invention relates to methods and apparatus for analysing spectra obtained from a sample to determine one or more organic compounds present in that sample.
  • Chemical analysis of a sample is used in many industries where determination of the composition of a sample is important.
  • one such area where chemical analysis of a sample is used is in proteomics where the structure and function of proteins is studied.
  • mass spectrometry One technique for chemical analysis of a sample is mass spectrometry.
  • mass spectrometry a sample is ionized to generate charged molecules or molecule fragments and the abundance of particular molecules or molecule fragments is determined according to the mass-to-charge ratio of the molecules or molecule fragments.
  • the abundances of molecules or molecule fragments with particular mass-to-charge ratios can be processed to generate a spectrum and the mass-to-charge ratios indicated in the spectrum can be analysed to determine substances present in the sample.
  • the taxonomy of proteins in the sample may be known. For example, it may be known that all proteins in the sample originate from a particular organism. However a particular taxonomy may include a very large number of proteins. Identifying which proteins in a particular taxonomy are present in a sample is therefore computationally expensive. As such, it has proved difficult to provide fast methods for determining whether a particular protein is included in a sample, based upon a spectrum generated from the sample and a predicted spectrum of the protein of interest.
  • Tandem mass spectrometry machines are machines capable of performing multistage mass spectrometry in which a fragment identified in a first spectrum can be selected and further fragmented to produce a second spectrum. Where a large number of fragments are identified in a first spectrum, real-time analysis of all fragments by producing respective spectra may not be possible. Methods for directing the selection of fragments of a first spectrum which are to be used as a basis for the generation of respective second spectra are therefore desirable.
  • US 2006/085141 A1 discloses fitting measured mass spectral data to simulated spectra. Isotopic distributions are calculated and compared to the measurements. Chemical compounds are thus identified.
  • a method of determining an indication of the presence or absence of a substance in a sample comprises receiving data generated from the sample, the received data comprising a plurality of data points, each data point having an associated value.
  • Theoretical data points associated with the substance are obtained.
  • a probabilistic score is determined, the probabilistic score being determined based upon a range of values, the range being defined relative to the associated value.
  • the theoretical data points and the plurality of data points are processed to determine correspondence between the theoretical data points and the plurality of data points.
  • a strength of the correspondence is determined, the strength of the correspondence being based upon the probabilistic scores.
  • the presence or absence of the substance is determined based upon the strength of the correspondence.
  • the probabilistic score determined for each data point is based upon a range of values determined with reference to a value associated with that data point. That is, the range of values is defined specifically with reference to the associated value. It has been found that using a range which is defined with reference to an associated value provides accurate indications of the presence or absence of a substance in a sample, and is preferred to a method, in which, for example, a range is predefined without reference to the value associated with a particular data point.
  • the value associated with each of the data points in the received data may be a mass or mass to charge ratio.
  • Each data point may indicate an abundance of chemical species (e.g. ions or molecules) having the associated mass or mass to charge ratio. That is, the received data may define a spectrum such as a spectrum produced by a mass spectroscopy apparatus.
  • the range may be a range centred upon the associated value.
  • a suitable range may be defined by the associated value plus or minus a mass value in the range 0.01 Da to 0.3Da.
  • the range may be defined as a proportion of the associated value.
  • Each of the theoretical data points may have an associated mass or mass to charge ratio, and each theoretical data point may indicate an abundance of chemical species having the associated mass or mass to charge ratio.
  • the probabilistic score may be based upon a number of theoretical data points having an associated value within the range defined with reference to that data point. For each of the data points, the probabilistic score is based upon the number of theoretical data points associated with the substance.
  • Each theoretical data point specifies a probabilistic value for the associated value.
  • the theoretical data points may be associated with a class of substances of which the substance is a member.
  • the class of substances may be a set of proteins occurring in organisms within a particular taxonomy or set of taxonomies.
  • Processing the theoretical data points and the plurality of data points to determine correspondence between the theoretical data points and the plurality of data points may comprise determining a shared peak count, the shared peak count indicating a number of the plurality of data points having an associated value that corresponds to an associated value associated with one of the theoretical data points.
  • Correspondence between a data point and theoretical data points may be defined with reference to said range which is based upon the value associated with that data point.
  • Determining a strength of correspondence may comprise generating a further probabilistic score based upon the probabilistic scores associated with each of the data points and the shared peak count.
  • Generating the further probabilistic score may comprise multiplying the probabilistic scores associated with each of the data points.
  • the method may further comprise enumerating a plurality of combinations and summing or multiplying the probabilistic scores based upon the enumerated combinations.
  • Enumerating the plurality of combinations may comprise storing a first combination and performing one or more bit-shift operations to enumerate others of the combinations.
  • bit shift operations can be implemented using software, or alternatively can be implemented in hardware, for example using a field programmable gate array (FPGA).
  • the further probabilistic score may indicate a likelihood that a number of theoretical data points indicated by the shared peak count have associated values which correspond to associated values of the received data points.
  • the further probabilistic score may indicate a likelihood that a number of theoretical data points equal to or greater than the shared peak count by chance have associated values which correspond to associated values of the received data points.
  • the further probabilistic score may indicate a likelihood that a first plurality of the data points have an associated value that correspond to an associated value associated with one of the theoretical data points and others of the plurality of data points have associated values that do not correspond to an associated value associated with one of the theoretical data points.
  • some of the data points will in general match theoretical data points associated with at least some known substances.
  • the match between theoretical data points associated with a known substance and the data points generated from the sample may be caused by the presence of a known substance in the sample, or may be a chance match. That is, the data points generated from the sample are independent of the theoretical data points associated with the known substance unless the known substance is present in the sample, but matches will often be generated for known substances which are not present in the sample.
  • Each of the further probabilistic scores is an indication of how likely it is that the observed degree of correspondence between the data points generated from the sample and the theoretical data points associated with a substance occurred by chance.
  • the received data may be spectral data.
  • Each data point in the received data may be a peak in the spectral data.
  • the sample may comprise at least one chemical entity selected from the group consisting of proteins, peptides, carbohydrates and metabolites.
  • the substance may be selected from the group consisting of proteins, peptides, carbohydrates and metabolites.
  • the theoretical data associated with the substance may comprise spectral data. Each theoretical data point may be a peak in the spectral data.
  • a method for processing spectral data comprising a plurality of processing phases.
  • the method comprises executing processing of a first processing phase on a first processing element and executing processing of a second processing phase on a second processing element.
  • the spectral data may be processed by the first processing phase at a first time and the spectral data may be processed by the second processing phase at a second time subsequent to the first time.
  • the first and second processing elements may operate in series such that particular spectral data is first processed by the first processing element and subsequently processed by the second processing element. In this way particular processing to which the spectral data is to be subjected is divided between the first and second processing elements, thereby increasing its efficiency.
  • the first processing phase may process first spectral data and the second processing phase may process second spectral data, the second spectral data being different to the first spectral data. That is, where first and second different spectral data is to be processed, the first and second spectral data may be processed by different processing elements, thereby improving the efficiency of the processing.
  • the processing may comprise carrying out a method according to the first aspect of the invention.
  • a mass spectrometry data analyser for processing spectral data in a plurality of processing phases.
  • the mass spectrometry data analyser comprises a first processor arranged to execute processing of a first processing phase and a second processor arranged to execute processing of a second processing phase.
  • a method of processing a sample to determine an indication of the presence or absence of a substance in the sample, the substance having associated theoretical data comprises receiving a first data value indicating a first degree of correspondence between the theoretical data and data generated from the sample at a first time.
  • a second data value indicating a second degree of correspondence between the theoretical data and data generated from the sample is received at a second time.
  • An indication of the presence or absence of the substance in the sample is determined based upon the first degree of correspondence and the second degree of correspondence.
  • the fourth aspect of the invention provides a method in which first and second data values are used in a process arranged to determine the presence or absence of a substance in a sample.
  • the first and second data values are preferably data values of the same type and are determined from a sample in the same way.
  • Determining an indication of the presence or absence of the substance in the sample may comprise determining that the substance is present in the sample if both of the first data value and the second data value exceed a predetermined threshold. That is, the parameter indicated by the first and second data values may need to exceed the threshold for at least a predetermined time before it is determined that the substance is present in the sample.
  • Determining an indication of the presence or absence of the substance in the sample may be based upon a difference between the first and second data values.
  • Determining an indication of the presence or absence of the substance in the sample may be based upon a rate of change indicated by the first and second data values.
  • the method may further comprise processing the first and second data values to determine whether to carry out processing to receive a third data value indicating a third degree of correspondence between at least one of the theoretical data points and data generated from the sample at a third time.
  • Determining whether to carry out processing to receive a third data value may be based upon rate of change between the first and second data values.
  • Determining whether to carry out processing to receive a third data value may be based upon a relationship between the first and second data values and a threshold value.
  • a further substance may be selected and the sample may be further processed to determine an indication of the presence or absence of the further substance in the sample.
  • the data generated from the sample may be spectral data.
  • the data generated from the sample may comprise a plurality of peaks.
  • the sample may comprise at least one chemical entity selected from the group consisting of proteins, glycoproteins, peptides, carbohydrates and metabolites.
  • the substance may be selected from the group consisting of proteins, glycoproteins, peptides, carbohydrates and metabolites.
  • the theoretical data associated with the substance may comprise spectral data.
  • the spectral data may comprise a plurality of predicted masses.
  • the method may further comprise graphically displaying the first data value and the second data value to a user.
  • the first data value and second data value may be displayed to the user whilst the sample is still being processed, or subsequent to the sample being processed.
  • the data value may be generated at a plurality of times, and the data values at the plurality of times may be displayed as part of a graph indicating change in the data value overtime.
  • a method for analysing the composition of a sample comprises generating a first spectrum based upon the sample and selecting a peak in the first spectrum to form the basis for further analysis.
  • a second spectrum is generated based upon chemical entities represented by the selected peak.
  • the second spectrum is processed to determine an indication of the presence or absence of a substance in the sample by carrying out a method according to the fourth aspect of the invention.
  • the method may further comprise selecting a second peak in the first spectrum and generating a further second spectrum based upon chemical entities represented by the second peak.
  • the further second spectrum may be processed to determine an indication of the presence or absence of a substance in the sample.
  • the method may further comprise generating a further second spectrum based upon chemical entities represented by the selected peak and processing the further second spectrum to determine an indication of the presence or absence of a substance in the sample.
  • the method may further comprise selecting a third peak in the second spectrum to form the basis for further analysis, generating a third spectrum based upon chemical entities represented by the third peak and processing the third spectrum to determine an indication of the presence or absence of a substance in the sample.
  • the method may further comprise graphically displaying the selected peaks to a user. For example, peaks may be displayed to a user and those peaks selected for further processing may be highlighted, for example using a predetermined colour to indicate selected peaks.
  • the indication satisfies a fourth criterion it may be determined that no further spectra based upon chemical entities represented by a selected peak should be generated.
  • Chemical entities may be, for example, fragments of a peak.
  • likelihood and probabilistic score are used to indicate any probabilistic quantitative measure, including for example a probability, or a mathematical likelihood.
  • a detector 1 is arranged to detect ions generated from a sample 2. Ions are generated from the sample 2 by an ion source 3 which ionizes molecules in the sample.
  • a mass analyser 4 is arranged to separate ions generated by the ion source 3 according to the mass-to-charge ratio (m/z) of the ions such that the detector 1 can measure a quantity of ions with a particular mass-to-charge ratio and provide data to a computer 5.
  • the computer 5 is arranged to communicate with a database 6 to store data based upon the data received from the detector 1.
  • a suitable mass analyser is the LCMS-IT-TOF mass spectrometer of Shimadzu Corporation, Kyoto, Japan, although any suitable mass spectrometry system may be used such as an LC-ESI (liquid chromatography - electrospray ionisation) ion trap TOF (time of flight) mass spectrometer.
  • LC-ESI liquid chromatography - electrospray ionisation
  • TOF time of flight
  • Figure 2 shows an example spectrum generated by an apparatus such as the spectral analysis system of Figure 1 .
  • the spectrum has a plurality of peaks such as peaks 10, 11.
  • Each peak is defined by a mass-to-charge ratio indicated on the x-axis and an associated intensity indicated on the y-axis.
  • a peak with a relatively high associated intensity such as peak 10 indicates a relatively high abundance of ions with the mass-to-charge ratio associated with the peak 10, while the peak 11 indicates a relatively low abundance of ions having the mass-to-charge ratio associated with the peak 11.
  • a spectrum such as the spectrum of Figure 2 can be used to identify a substance contained within the sample 2 of Figure 1 .
  • the sample is first processed to cleave the substance along some of its bonds to generate fragments.
  • each fragment generated by the cleavage generates a peak in a spectrum such as peaks 10 and 11 of Figure 2 . It is possible to predict fragments that will be generated from cleavage of a known substance based upon the method used to cleave the substance. The corresponding mass-to-charge ratio of the predicted fragments can also be predicted.
  • the peaks in a spectrum generated from a sample can be used to identify a substance present in the sample based upon predicted fragments for a plurality of substances.
  • the sample is a protein
  • the sample is cleaved using a restriction enzyme (for example Trypsin) which breaks the sample at predictable points along the amino acid chain.
  • a restriction enzyme for example Trypsin
  • PMF peptide mass fingerprinting
  • spectra generated from a sample may be generated from a sample containing more than one substance, or from a sample containing contaminants. Fragmentation may not occur exactly as predicted and with electrical and chemical noise, large numbers of peaks may be generated. Identification of substances present in a sample may therefore be difficult.
  • FIG. 3 A tandem mass spectrometry system for identification of substances is shown in Figure 3 .
  • the system of Figure 3 can be viewed as an extension of the system of Figure 1 as components 1A to 5A of Figure 3 correspond to components 1 to 5 of Figure 1 and are arranged in such a way that the system of Figure 5 can operate in the same way as the system of Figure 1 .
  • an ion selector 17 (which in some embodiments takes the form of an ion trap such as a quadrupole ion trap) is arranged to receive ions from mass analyser 4A.
  • Computer 5A communicates with ion selector 17 to direct the selection of an ion, separated from other ions by mass analyser 4A, for further fragmentation by an ion fragmentation component 18.
  • Ions generated by the ion fragmentation component 18 are further separated by the mass analyser 4A and detected by detector 1A in the same way as described above with reference to the system of Figure 1 .
  • the selection of ions by ion selector 17 after separation by mass analyser 4A and subsequent analysis may be repeated a plurality of times.
  • An ion selected by ion selector 17 shall be referred to as a precursor ion, and ions generated by ion fragmentation of the selected precursor ion (carried out by the ion fragmentation component 18) shall be referred to as product ions. It can be noted that an ion may be both a product ion of a precursor ion and a precursor ion of several product ions.
  • the process of generating a spectrum using a system such as the system of Figure 1 shall be referred to as MS 1 .
  • the process of generating spectra in general shall be referred to as MS" where n - 1 indicates the number of precursor ions that are in the chain of ions fragmented to generate the current spectrum in the system of Figure 3 .
  • MS 2 shall be used to refer to generation of one or more spectra from one or more precursor ions identified in an MS 1 spectrum (i.e. the ion selector 17 operates on ions of a single spectrum generated by the mass analyser 4A).
  • FIG 4 illustrates ion selection and spectra generation according to the system of Figure 3 .
  • Two MS n+1 spectra 20a, 20b are generated from respective precursor ions 21a, 21b indicated in an MS n spectrum 22.
  • Spectrum 20a is generated from an ion 21a and spectrum 20b is generated from an ion 21b.
  • Spectra 20a, 20b can be used to identify particular ions present in the sample used to generate MS n spectrum 22.
  • analysis of the spectrum 20a allows information to be obtained as to the ion responsible for the peak 21a in the spectrum 22. Such information is useful in determining the composition of the sample which resulted in generation of the spectrum 22.
  • FIG 4A shows the computer 5 of Figure 1 in further detail, although it will be appreciated that the computer 5A of Figure 3 generally has the same form.
  • the computer comprises a CPU 5a which is configured to read and execute instructions stored in a volatile memory 5b which takes the form of a random access memory.
  • the volatile memory 5b stores instructions for execution by the CPU 5a and data used by those instructions. For example, in use, output from the detector 1 may be stored in the volatile memory 5b.
  • the Computer 5 further comprises non-volatile storage in the form of a hard disc drive 5c.
  • the output from the detector 1 may be stored on the hard disc drive 5c.
  • the computer 5 further comprises an I/O interface 5d to which are connected peripheral devices used in connection with the computer 5. More particularly, a display 5e is configured so as to display output from the computer 5.
  • the display 5e may, for example, display a representation of the output from the detector 1. Additionally, the display 5e may display graphical representations of results of processing the output from the detector 1.
  • Input devices are also connected to the I/O interface 5d. Such input devices include a keyboard 5f and a mouse 5g which allow user interaction with the computer 5.
  • a network interface 5h allows the computer 5 to be connected to an appropriate computer network so as to receive and transmit data from and to other computing devices.
  • the CPU 5a, volatile memory 5b, hard disc drive 5c, I/O interface 5d, and network interface 5h, are connected together by a bus 5i.
  • the cleavage process will generate a plurality of peptides. These peptides are then further fragmented by the ion fragmentor 18 which may be a collision cell.
  • Figure 5 shows fragments that may be generated from a peptide 14. Lines 15 indicate bonds of the peptide 14 that may be broken and the arrows of lines 15 indicate corresponding ions that are generated after the peptide is processed by the ion source 3 of Figure 1 .
  • One method of breaking bonds in a sample which may be implemented by the ion fragmentor 18 is low energy Collision Induced Dissociation (CID) and it is known that using CID, peptides and polypeptides tend to fragment into b and y ions.
  • CID Collision Induced Dissociation
  • Figures 6A and 6B show spectra that are predicted to be generated from a peptide with amino acid sequence FIAGGER comprising amino acids represented by their standard IUPAC abbreviations F, I, A, G, E and R.
  • the predicted spectra are based on a fragmentation of the substance using CID and as such b and y ions are predicted to be generated and detected.
  • Each peak in each of the spectra corresponds to a predicted ion fragment, with Figure 6A showing predicted b-ions and Figure 6B showing predicted y-ions.
  • the mass of an ion b 3 corresponds to the mass of an ion b 2 with an additional mass corresponding to the mass of amino acid I.
  • Ion b 3 can therefore be deduced to have the same structure as ion b 2 with an additional I amino acid group.
  • Other additional amino acid groups can also be determined by the difference in mass between detected ions and are indicated between ion peaks.
  • Corresponding y-ions for each b-ion are shown in Figure 6B .
  • MS n processing for n greater than or equal to 1, for identification of a substance will now be described at a high level with reference to Figure 7 .
  • the received sample may be a polypeptide or mix of polypeptides, or other molecules such as metabolites.
  • the received sample is a precursor ion generated from MS n-1 processing, which is further fragmented to generate product ions, and the substance to be identified is the precursor ion.
  • the ions are separated according to the mass-to-charge ratio of the ions and at step S4 the separated ions are detected and the abundance of each ion, generated at step S2, is determined.
  • the determined abundances of ions, together with their respective mass-to-charge ratios produce a spectrum such as the spectrum described above with reference to Figure 2 .
  • the generated spectrum is processed to determine at least one substance present in the sample received at step S1.
  • a probability based score providing an indication of a probability that the peaks P S match predicted peaks associated with any substance of the same type as D purely at random at least as well as the peaks P S match a set of predicted peaks P D associated with D is generated.
  • a substance can be considered to be of the same type as D based upon any suitable criterion.
  • a taxonomy or set of taxonomies associated with D such as the same class, the same family or the same species
  • has a particular number of peptides or ion fragments based upon the number of peptides or ion fragments associated with D or both.
  • the set of predicted peaks P D is determined based upon the expected peaks associated with D, the expected peaks being determined from the mass-to-charge ratio of expected ions or peptides associated with D determined as described above with reference to Figure 3 .
  • a shared peak count k indicating the number of peaks in the set P S that match a peak in the set P D is determined.
  • a probability R(h) of any h peaks in P S matching predicted peaks associated with any substance of the same type as D is generated, based upon a probability associated with each peak in the set P S occurring in any spectrum generated from a sample which includes a substance of the same type as D.
  • the likelihood R(h) for each number of peaks h is summed to determine a likelihood L of any substance of the same type as D matching the peaks in the set P S less well than the match between P S and P D .
  • a value P val (k), determined by the value (1 - L), indicates the likelihood that peaks associated with any substance of the same type as D will match the set of peaks P S at least as well as the set of peaks P D match the set of peaks P S (i.e. will match k or more peaks).
  • the value P val (k) is a measure of the quality of the match between D and the spectrum generated from the sample. Smaller values of P val (k) indicate a better match (and thus a greater likelihood that D is in the sample) than larger values of P val (k).
  • the likelihood associated with each peak in the set P S occurring in any spectrum generated from a sample is determined from a probability density function (PDF) associated with the type of D.
  • PDF probability density function
  • a PDF value for a peak having a particular mass-to-charge ratio is generated from a database of known substances of the same type as D by determining predicted fragments for each known substance in the database and determining how likely it is that a spectral peak will occur at the mass-to-charge ratio of the peak generated from the sample, based upon the mass-to-charge ratio of the predicted fragments.
  • Each mass-to-charge ratio that may be detected by the particular instrument used will have a particular PDF value, based upon the number of fragments predicted from substances in the database having the particular mass-to-charge ratio.
  • High accuracy machines are able to detect a large number of separate mass-to-charge ratios, and the variation between PDF values associated with different mass-to-charge ratios is likely to be greater than for low accuracy machines.
  • a PDF table associated with various types may be generated and stored and used to look up PDF values for particular peaks.
  • PDF values are specific to a particular restriction enzyme such as trypsin.
  • PDF values may be determined based upon a predetermined number of cleavages that may be missed. Larger numbers of possible missed cleavages generate larger numbers of possible peptides. One possible missed cleavage has been found to be a suitable number.
  • a summed PDF table is produced for some ranges of numbers of fragments.
  • PDF values vary considerably and as such, individual numbers of peptides each have their own PDF table.
  • Suitable ranges of numbers of peptide fragments in a polypeptide per PDF table have been found to be one PDF table per number of peptide fragments for numbers of peptides in the range 1 to 19, two PDF tables for numbers of peptides in the range 20 to 30 and increasing ranges of numbers of peptides for peptide numbers greater than 30.
  • PDF tables have been found to be suitable for ranges of numbers of peptides although other numbers of PDF tables and ranges of numbers of peptide in a polypeptide per PDF table may be suitable.
  • PDF tables for ion fragments are determined in a similar manner, based upon the frequencies of different numbers of ion fragments.
  • Predicted and experimentally observed peaks tend to fall in clusters in a small range of masses out of the total range of possible masses.
  • An example mass cluster is shown in Figure 8 for the range 998.3 to 998.8 with the number of predicted peaks indicated on the y-axis and the mass indicated on the x-axis.
  • a continuous line 25 indicates the actual number of predicted peaks for each mass and a line 26 indicates the kernel density estimation of the PDF. It can be seen that the actual number of predicted peaks for a particular mass within the cluster varies considerably, with a point 27 indicating that peaks indicating a mass of approximately 998.46 are relatively common compared to a point 28.
  • step S10 the set of peaks P S determined from the sample S is received, each peak p i in the set P S having an associated mass-to-charge ratio m i (for i in the range 1 to the total number of peaks ⁇ in the set P S ).
  • m i mass-to-charge ratio
  • all generated ions have a charge of '1' and as such m i indicates mass. This may be for example due to the ions having been pre-processed to form a singly-charged peak list.
  • the set of peaks P S may include all peaks present in the spectrum or may include a subset of the peaks present in the spectrum selected, for example, a subset of peaks of greatest intensity.
  • the set of predicted peaks P D associated with the substance D is received and at step S12 a tolerance ⁇ is received.
  • the tolerance ⁇ is a tolerance used in the determination of matches between peaks in the set P S and peaks in the set P D , and more particularly is a tolerance between masses associated with peaks in the set P S and masses associated with peaks in the set P D required for peaks in the sets P S and P D to be considered to match.
  • the number of peaks k in the set P S that match a peak in the set P D within the tolerance ⁇ is determined.
  • a peak p i in the set P S is selected and at step S15 a value C(p i ) is determined for the peak p i according to equation (1):
  • the denominator of the fraction of equation (1) gives the sum of all PDF values in the PDF table associated with D and the numerator gives the sum of the values in the PDF table with a mass within tolerance ⁇ of the mass m i associated with the peak p i .
  • the value C(p i ) therefore is the probability of a peak with mass m i , within tolerance ⁇ , occurring in a spectrum generated from any substance of the type associated with D.
  • the value (1-C(p i )) is the probability that any single peak associated with a substance of type D does not match with the peak p i .
  • the value (1-C(p i )) y is therefore the probability that y peaks associated with a substance D do not match a peak with mass m i , derived by assuming that matches are entirely random and as such will follow the statistics of Bernoulli trials. It follows that the value C ⁇ 1 (p i ) is the probability that one or more peaks of the y peaks associated with a substance of type D matches the peak p i .
  • step S17 a check is carried out to determine if there are more peaks in the set P S that have not been processed according to the processing of steps S15 and S16. If it is determined that there are more peaks to be processed then processing continues at step S14 where a previously unprocessed peak is selected from the set P S . Otherwise it is determined that a probability of a peak matching at least one peak in a substance of type D has been determined for each peak in the set P S and processing continues at step S18.
  • the value given by the term inside the summation of equation (3) is the probability of a particular combination of h peaks in the set P S matching predicted peaks associated with any particular substance of the type associated with D, together with the remaining (n - h) peaks in the set P S not matching peaks associated with the particular substance of the type associated with D.
  • the summation of equation (3) sums across all possible combinations of h matches.
  • the value R(h) is therefore a probability of any combination of h peaks in the set P S matching peaks of a substance of the type associated with D, and the remaining peaks in the set P S not matching a peak of the same substance.
  • step S21 the counter variable h is incremented and processing returns to step S19.
  • Equation (4) gives a likelihood of a match between peaks of any substance of the type associated with D and peaks in the set P S that is not as good as the match between peaks in the set P S and peaks in the set P D . That is, the summation provides a likelihood of a match of any h peaks, where h is less than k.
  • the value P val (k) is therefore the likelihood of a match between peaks in the set P S and predicted peaks of a substance of the type associated with D that is at least as good as the match between the peaks in the set P S and the peaks in the set P D .
  • the value P val (k) is a measure of the quality of the matches between the set P S and the set P D .
  • a greater value of P val (k) indicates a greater likelihood that the peaks in the set P S match peaks of any substance of the type associated with D. As such, where the value of P val (k) is high, there can be less confidence that matching an arbitrary k peaks in the set P S to peaks associated with the substance D is a reliable indicator of the strength of the match between the sets of peaks P D and P S , and can be used as an indicator of whether or not the substance D is present in the sample S.
  • the value R(h) of equation (3) is determined for values of h less than k in order to determine the value P val (k). It will however be appreciated that a value R(k) can be determined according to equation (3) where k is the peak match count between D and the set P S .
  • the value R(k) is itself a reliable indicator of the strength of the match between the sets of peaks P D and P S and can be used in the place of or as well as the value P val (k) in any of the methods described.
  • a value Score(k) can be determined from the value P val (k) according to equation (5) below.
  • Score k ⁇ log P val k
  • Greater values of Score(k) indicate a greater likelihood that D is present in the original sample (given the definition of P val (k) as set out above), and the value Score(k) can be thresholded to determine the presence or absence of D in the sample.
  • the threshold may be a predetermined value or may be determined as the minimum value that allows a substance to be identified with a particular confidence based upon the selected parameters (including for example the taxonomy). It will of course be appreciated that the value P val (k) may be thresholded and used in the determination in the same way as Score(k).
  • a value Score(k) may be determined for each of a set of peptides or polypeptides ⁇ and the associated value Score(k) for each member of ⁇ used to determine the elements of ⁇ that are most likely to be present in the sample S.
  • the set ⁇ may be determined by first performing a peptide mass fingerprint (PMF) technique with the set of peaks P S and sets of predicted peaks associated with database proteins. Any database protein with a predetermined number of predicted peaks that match peaks in P S within a predetermined threshold (that is that have a shared peak count greater than the threshold) is added to the set ⁇ and therefore considered for processing to determine a value Score(k) where k is equal to the number of matching peaks.
  • PMF peptide mass fingerprint
  • the value P val (k) does not depend upon the particular k peaks that are matched between P S and P D and as such for any substance D' that is of the same type as D that has any combination of k peaks matching peaks P S , the value P val (k) will be the same as the value P val (k) for the substance D.
  • a value R pattern (k) can be calculated to indicate a likelihood of the particular pattern of k matches between the set of peaks P S and the set of peaks P D associated with a substance D according to equation (6):
  • R pattern k ⁇ i ⁇ H C ⁇ 1 p i ⁇ ⁇ j ⁇ P 1 ⁇ C ⁇ 1 p j
  • the right hand side of equation (6) is the same as the term inside the summation of equation (3), but here the set H includes the k peaks of the set P S which matched peaks associated with the substance D.
  • the value R pattern (k) takes into account likelihoods of the actual matching peaks.
  • the value of equation (6) can be used to resolve which substance of two substances D and D', both of the same type, is the most likely to be present in a sample in the case where D and D' both have the same number of matching predicted peaks to the set P S , depending upon the values R pattem (k) generated based upon each of D and D'.
  • the value Score(k) associated with a substance D can be used to direct real-time data acquisition and ion selection for further processing such as the ion selection illustrated in Figure 6 .
  • determination of the value R(h) of equation (3) and therefore the value Score(k) of equation (5) in around 10 milliseconds as is required for real-time data acquisition in a standard way would require computation of the order of 10 5 Teraflops and is therefore impractical with current computing power.
  • a fast method for determining the value R(h) (and therefore the value P val (k)) will therefore now be described.
  • the set P S is divided into q subsets T 1 to T q of approximately equal size, where q is approximately equal to the square root of the number ⁇ of peaks in P S .
  • the size of each subset is referred to in the following description as N i .
  • an associated value R(t) is determined, for all possible values of t in the range 0 to N i (or 0 to k where k is less than N i ). This corresponds to all possible combinations of k or less matched peaks in the subset.
  • a particular value inside the summation of equation (3) can be determined by selecting a single value R(t i ) associated with each of the subsets T 1 to T q where i is equal to 1 to q and indicates the associated subset, such that the sum of the values t i is equal to the number of matches k between predicted peaks associated with D and peaks in the set P S .
  • the value R(h) can therefore be determined by evaluating the summation of equation (3), each iteration of the summation being evaluated with reference to appropriate values of R(t i ).
  • the method for determining the value R(h) described above is now illustrated by way of an example with reference to Figure 10 .
  • the example of Figure 10 is based upon a value of k (indicating the number of matching peaks) equal to 5 and a value of ⁇ (indicating the number of peaks in the set P S ) equal to 15.
  • the set P S is shown in Figure 10 , where its elements are indicated by 15 boxes 30, 5 of which are indicated as '1' and 10 of which are indicated as '0' as determined by the values k and ( ⁇ -k) respectively.
  • Each of the boxes 30 represents a peak in the set P S as indicated by labels 31 and the '0' or '1' indication in each box indicates whether the corresponding peak is considered to be a matching peak or a not matching peak.
  • the particular combination of '0' and '1' indications of combination 32 corresponds to one of the possible combinations of k matches required for the summation of equation (3).
  • Processing is carried out to determine sets of values for each of the sets T 1 to T 3 , corresponding to a value R(t) for each possible number of matching peaks in the set.
  • a value T 11 shown in Figure 10 is determined by summing values t 111 , t 112 ,..., t 11n each corresponding to a possible combination of a single matching peak in the set T 1 , and each corresponding to one of the possible combinations inside the summation of equation (3).
  • Each value t 112 ,..., t 11n is determined by multiplying values C ⁇ 1 (p i ) for each p i indicated as '1' (i.e.
  • t 122 C ⁇ 1 p 15 ⁇ 1 ⁇ C ⁇ 1 p 14 ⁇ C ⁇ 1 p 13 ⁇ 1 ⁇ C ⁇ 1 p 12 ⁇ 1 ⁇ C ⁇ 1 p 11
  • All values T ij for i equal to 1 to 3 and j equal to 0 to 5 are determined.
  • T 11 x T 22 x T 32 gives one of the possible values R(h).
  • Predetermining the values in the sets of values T ij saves a large number of multiplication operations as the multiplication operations are not repeated each time a value t ijf is used to determine one of the possible combinations.
  • All possible bit combinations in each of the sets T ij can be determined using a bitshift operation.
  • Bitshift operations included in languages such as C and C++ are fast and the speed of the operation can be exploited to quickly enumerate all possible combinations in a set T ij .
  • Use of a bitshift operation to enumerate all possible combinations of the set T 11 of Figure 10 is shown in Figure 10A .
  • An array of values P 1 corresponding to the value C ⁇ 1 (p i ) for each peak is determined and an array of values P 0 corresponding to the value 1 - C ⁇ 1 (p i ) for each peak is determined.
  • the set T 11 corresponds to all combinations of matches and non-matches in the set of peaks p 15 to p 11 in which a single peak is matched and all other peaks are not matched.
  • An initial arrangement of values satisfying the condition required for the set is enumerated, shown as t 115 .
  • the value t 115 is determined by multiplying the corresponding value from the set P 1 for each bit of t 115 that has a '1' indication and multiplying the corresponding value from the set P 0 for each bit of t 115 that has a '0' indication, and the value t 115 is stored.
  • the next arrangement of values. t 114 that satisfies the condition of the set T 11 is generated from t 115 by shifting the set of values one place to the left, using the bitshift operation (indicated by ⁇ in Figure 10A ) and the value t 114 is determined by multiplying values from the sets P 1 and P 0 selected in the same way as for the enumeration t 115 .
  • the value T 11 is determined by multiplying each of the enumerations t 115 to t 111 . It will be appreciated that by appropriately initialising an arrangement of values for the set t 11 and performing, in turn, as many bit shifts as there are elements in the set t 11 all necessary arrangements of values can be effectively enumerated.
  • the techniques described above for calculating values required in the calculation of R(h) can be efficiently implemented in software given that they provide a convenient mechanism for enumerating required combinations which relies upon the bit shift operator which is provided in the instruction set of most microprocessors and can be executed in a low number of clock cycles.
  • the various combinations may be enumerated using an appropriate logic circuit which can be arranged to generate the necessary combinations and use logical operators to determine the multiplications that are to be carried out to determine the value R(h).
  • Such a logic circuit can be conveniently implemented using a Field Programmable Gate Array (FPGA).
  • FPGA Field Programmable Gate Array
  • FIG. 11 a particular arrangement of the sample 2 and ion source 3 of Figure 1 is shown (it will be appreciated that the arrangement of Figure 11 can be similarly applied to the sample 2A and ion source 3A of Figure 3 ).
  • the sample 2 is first passed through a liquid chromatography column 35 where molecules in the sample are separated according to their retention time through the chromatography column 35.
  • An electrospray 36 corresponding to ion source 3, 3A generates ions from the molecules that have passed through the chromatography column 35.
  • the generated ions are passed to the mass analyser 4 of Figure 1 (or correspondingly the mass analyser 4A of Figure 3 ) for processing as described above.
  • the electrospray 36 generates ions constantly as molecules of the sample pass through the chromatography column 35 and the mass analyser 4 therefore receives ions generated from molecules having substantially the same retention time.
  • FIG. 11A A further arrangement of the sample 2 and ion source 3 of Figure 1 is shown in Figure 11A .
  • the system of Figure 11A is a Matrix-assisted laser desorption/ionization system (MALDI).
  • MALDI Matrix-assisted laser desorption/ionization system
  • the sample 2 and LC column 35 of Figure 11A correspond to the sample and LC column of the system of Figure 11 .
  • molecules separated according to their retention time through the chromatography column 35 are spotted onto a plate 36A in which different spots correspond to different retention times.
  • the retention time of each spot can be determined based upon the location of the spot on the plate.
  • the plate may be stored and processed at a later time (sometimes referred to as offline processing).
  • a value Score(k) is determined a plurality of times for a substance of interest D based upon a set of peaks P S generated from the sample at each time.
  • Each of the determinations of the value Score(k) shall be referred to as a scan.
  • the set of peaks P S may be a set of peaks generated independently of the previous set of peaks or may be a cumulative set of peaks determined based upon the set of peaks P S from a previous scan or a plurality of previous scans, together with data determined in the current scan. In the case where P S is a cumulative set of peaks the signal to noise ratio will generally be improved.
  • the value Score(k) (or any other suitable metric including R(k)) is used to determine whether D is present in S in the usual way, by determining whether the value Score(k) associated with D exceeds a threshold. Additionally, values of Score(k) from preceding scans can be used to make decisions in relation to the substance D.
  • step S25 of Figure 12 data associated with the substance D is received for processing.
  • the substance D is a known peptide or polypeptide and has an associated set of predicted peaks P D .
  • step S26 the set of peaks P S determined from the sample S is received and at step S27 the value Score(k) is determined for the substance D as described above with reference to Figure 9 .
  • step S28 it is determined if further processing is required based upon an updated set of peaks P S ' determined from the sample at a subsequent scan after the set of peaks P S was determined.
  • the set of peaks P S ' may be either a cumulative set of peaks based on P S or a fresh set of peaks as described above.
  • step S28 If it is determined at step S28 that further processing is required then at step S29 the set of peaks P S ' is determined and processing continues at step S27 where a further value Score(k) is determined based upon the updated set of peaks P S '. Otherwise at step S30 no further scans based upon substance D are performed. A determination of the presence or absence of substance D in the sample S may be output at step S30 or it may be determined that no determination can be made. Alternatively, a further substance may be selected based upon the result of the scans for substance D, for example a MS n+1 ion which is useful in identifying substance D.
  • any suitable metric calculated from the data other than Score(k), where the metric preferably uses more than one peak, can be used.
  • suitable metrics include P val (k) and R(k) described above, or known metrics such as a MASCOT score or a de novo sequencing score.
  • a line 37 indicates the value of the threshold for determining the presence of a substance in a sample (i.e. the threshold which Score(k) is required to exceed).
  • the threshold may be set based upon a significance test.
  • a suitable threshold is a value that determines an event is significant if it would be expected to occur at random with a frequency of less than 5%.
  • the value of the threshold shown in Figures 13A to 13J is 4.5 indicating a 1 in 31623 chance of the observed quality of match having occurred by chance.
  • Each graph is determined based upon a set of peaks P S determined from a sample S and based upon a substance D and associated set of predicted peaks P D .
  • Each plot indicates a value Score(k) determined at intervals of 0.25 seconds indicating the interval time between scans. It should be noted that the scale of the time axis in each of Figures 13A to 13F varies between the Figures 13A to 13F .
  • each of the graphs of Figures 13A to 13D indicate that the value Score(k) has exceeded the threshold 37 and subsequent scans show that the value Score(k) remains greater than the threshold. It can therefore be determined that each of the substances for which the value Score(k) is shown in each of Figures 13A to 13D do not require further scans to be performed and that there is a reasonable degree of certainty that the substance D is present in the analysed sample.
  • a determination for each of the substances of Figures 13A to 13D can be made at a point 38A, 38B, 38C and 38D respectively where it can be seen that the value Score(k) has exceeded the threshold and the value Score(k) has remained substantially constant for 2 scans
  • the number of scans required during which the score remains constant before a determination is made may be greater than 2, for example the number of scans required may vary according to the value Score(k). Additionally, in some embodiments further scans may be desirable so that the value Score(k) attains a higher score indicating a greater degree of confidence in the indication.
  • the graphs of Figures 13E and 13F indicate that the value Score(k) has not exceeded the threshold 37.
  • the value Score(k) in Figure 13E has a value of approximately 3 at a time 1sec and the value of Score(k) remains constant for a further 1.25secs and 5 scans. Since the value Score(k) does not change for 5 scans it is unlikely that the value Score(k) will increase further and it can be determined that no further scans should be performed.
  • the value Score(k) in Figure 13F decreases with a sharp gradient to a value 0 at a point 39 and again it can be determined that no further scans should be performed. Since the value Score(k) did not exceed the threshold 37 no positive determination can be made about the substance D for which the Score(k) is indicated in each of Figures 13E and 13F .
  • the graphs of Figures 13G and 13H each indicate that the value Score(k) has not exceeded the threshold 37 after 2secs and the value Score(k) is no longer increasing.
  • the graph of Figure 13I shows the value Score(k) reaching a peak after 2.5secs before decreasing after 3.5secs. It may therefore be determined that it is no longer desirable to perform further scans for each of the substances D of Figures 13G to I .
  • the graph of Figure 13J indicates that the value Score(k) exceeds the threshold 37 at a point 40. However, the value Score(k) decreases below the threshold 37 at a point 41 and the determination of exceeding the threshold may therefore be considered to be less confident that if the value Score(k) remained above the threshold 37 for a predetermined number of scans subsequent to exceeding the threshold.
  • Each of the substances D of Figures 13E to 13J may be used for directed data acquisition to select further substances which may confirm the presence of a substance related to substance D, such as a protein of which D is a peptide ion, as will be described in further detail below with reference to Figure 14 .
  • a substance related to substance D such as a protein of which D is a peptide ion
  • Various properties of the value Score(k) across a plurality of repeat scans at different time intervals can be used in the determination of whether further scans are required or desirable.
  • the rate of change of the value Score(k) i.e. the gradient of the plot of the value Score(k)
  • the rate of change may be used in combination with the value of Score(k), or any other useful measure of the value Score(k) may be used.
  • the set of peptides or polypeptides ⁇ may be determined based upon the retention time of the molecule at the particular time.
  • a predicted value for the retention time of each peptide or polypeptide in the database can be stored and used to determine peptides or polypeptides that are expected to be passed from the chromatography column 35 to the electrospray 36 of Figure 11 at a particular time.
  • a value Score(k) is determined for substances in a set ⁇ based upon a spectrum generated from a MS n-1 precursor ion (i.e. based upon an MS" spectrum). If the value Score(k) exceeds a predetermined threshold for at least one substance D in ⁇ then a list of substances that may have been a precursor ion of the substance D in the MS n-1 spectrum is generated. Based upon the list of substances that may have been a precursor ion of D, a list of further precursor ions is identified that either would be useful in determining the substance that resulted in the identified precursor ion or that should not be MS" processed.
  • the processing is divided into a part A which is non-directed data acquisition processing and a part B which is directed data acquisition processing.
  • a precursor ion ⁇ is selected from the MS n-1 spectrum.
  • the precursor ion may be selected based upon, for example, intensity of a peak associated with the precursor ion.
  • an MS" spectrum is generated from ⁇ and at step S37 a value Score(k) is generated for each substance in a set ⁇ in the usual way as described above with reference to Figures 7 and 9 .
  • a check is performed to determine whether the value Score(k) associated with at least one substance D in ⁇ exceeds a threshold of 4.5.
  • Each substance D identified at step S38 is a possible identification of the precursor ion ⁇ .
  • step S35 a further precursor ion ⁇ in the MS n-1 data is selected. Otherwise it is determined that useful information has been obtained from the MS" data associated with ⁇ and data directed acquisition can commence.
  • a set Precursor D (which is a set of proteins in the case where n is equal to 2) is determined based upon those substances D in ⁇ whose value Score(k) exceeded the threshold and at step S40 a set Product D (which is a set of peptides in the case where n is equal to 2) is determined based upon the set Precursor D .
  • the set Precursor D is a set of substances that are possibly identified by the presence of a substance D in the MS n-1 spectrum (i.e.
  • the set Precursor D does not necessarily correspond to a set of substances selected for MS n+1 analysis
  • the set Product D is a set of MS n-1 ions whose identification is useful in confirming the presence of a substance in the set Precursor D and are therefore a shortlist of masses for preferential MS" analysis.
  • 94% of peptides define less than 6 proteins, so in the case where n is equal to 2 where the set Precursor D is a set of possible proteins and each D in ⁇ is a peptide, each D whose value Score(k) exceeds the threshold will in general identify less than 6 possible proteins.
  • the set Product D may be determined by identifying product ions that are predicted from substances in the set Precursor D .
  • n is equal to 2 and the set Precursor D is a set of proteins for which a peptide D is predicted
  • the set Product D is a set of peptides other than D which are predicted from the predicted cleavage of proteins in the set of proteins Precursor D .
  • the set Product D may be determined by identifying predicted peptides that uniquely identify a protein in the set Precursor D or in any other suitable way.
  • an ion ⁇ ' is selected from the MS n-1 spectrum by ion selector 17 of the system of Figure 5 based upon the set Product D .
  • the ion ⁇ ' may be selected by determining a mass-to-charge ratio of an ion in the set Product D and selecting an MS n-1 ion that has a corresponding mass-to-charge ratio within a predetermined threshold.
  • an MS" spectrum is generated from the ion ⁇ ' (which is itself a precursor ion given that it is used to generate an MS n spectrum) and at step S43 a value Score(k) is generated for each substance in a set ⁇ '.
  • the set ⁇ ' may be selected based upon the set Product D or may be determined in any other suitable way.
  • the set ⁇ ' indicates substances that are likely to provide useful information relating to the composition of a sample.
  • a check is carried out to determine whether the value Score(k) associated with at least one substance D' in ⁇ ' exceeds a threshold of 4.5. If it is determined that there is no substance D' in the set ⁇ ' that has an associated value Score(k) that exceeds 4.5 then processing returns to step S41 where a further precursor ion is selected. Otherwise at step S45 the set Precursor D and the set Product D are updated based upon the substances D' identified as having a value Score(k) that exceeds the threshold.
  • n is equal to 2 (i.e. MS 1 and MS 2 spectra are generated).
  • n is equal to 2 (i.e. MS 1 and MS 2 spectra are generated).
  • a protein in a sample based upon a single peptide identification (i.e. a single product ion identification).
  • identification of a single peptide may identify a small number of proteins that may be present in the sample (in 94% of cases, less than 6). Identification of a further peptide generated from the sample (and identified by a peak in the MS 1 spectrum) will often identify which of the possible proteins is in fact present in the sample and caused the first identified peptide to be present in the MS 1 spectrum.
  • the set Precursor D is a set of substances that are possibly identified by the presence of a substance D in the MS n-1 spectrum and the set Product D is a set of MS n-1 ions whose identification is useful in confirming the presence of a substance in the set Precursor D .
  • the set Product D may be a set of MS n-1 ions whose identification is not useful.
  • the selection of a precursor ion ⁇ ' of step S41 based upon the set Product D may comprise selecting an ion whose mass-to-charge ratio does not correspond to the mass-to-charge ratio of an ion in the set Product D .
  • Such processing may be used, for example, where the set Precursor D is a set of proteins that have already been identified, and the set Product D may be, for example, a set of peptides that are unique to an already identified protein (i.e. a member of the set Precursor D ). In such a case, identification of peptides in the set Product D would not aid further identification of substances present in the input sample.
  • both a set of MS n-1 ions that are useful and a set of MS n-1 ions that are not useful may be stored (together with corresponding sets of precursor substances) and used in the selection of MS n-1 precursor ions.
  • Graphical displays may be used to indicate results from or progress of MS processing, for example during MS processing or after MS processing or during or after MS n processing, where n > 1.
  • graphs such as the graphs of Figures 13A to 13J may be generated and displayed to a user.
  • a graphical display (for example displayed on a display of the computer 5 of Figure 1 ) may be arranged to indicate candidate peptides and/or ion series of candidate peptides in a tabular format.
  • ions selected to form the basis of further processing using MS n+1 processing may be indicated using particular colours and peptides may be coloured dependent upon the strength of confidence that the peptide is present in the sample.
  • results of processing to identify substances in a sample can be displayed in a convenient way which allows a user to quickly and easily determine important results of the processing.
  • a list of peptides that have been identified as present in the sample may be displayed to a user, which may be updated during an MS run, and a list of proteins that may be present in the sample based upon the identified peptides may similarly be displayed to a user. Confidence that the protein is present in the sample may similarly be indicated using different colours.
  • a graphical display may be used to indicate decisions relating to selection of ions for MS n+1 processing made by the system. For example, a hierarchy of ions which have been selected for MS n+1 processing from an MS" spectrum may be displayed to a user so that the user is able to quickly determine a path of ion selections which result in a current MS n+1 ion selection.
  • the tree may again indicate confidence of peptide identification in the tree using, for example, colours.
  • a first processor 45 is arranged to receive and process a first set of data from the detector 1 at a first time according to the methods set out above.
  • the processor 45 corresponds to the computer 5 of Figure 1 or computer 5A of Figure 3 . If at a second time a second set of data is available for processing (for example a further peak or set of peaks eluting from an LC column) and the processor 45 has not completed processing to identify substances in the sample based upon the first data set, the second processor 46 processes the second data set in the manner set out above.
  • a further processor 47 is provided to process further data sets when both processors 45, 46 are processing previously received data sets. Further processors can be provided as required.
  • three processors 50, 51, 52 are arranged to process data sets in serial.
  • a first processor 50 is arranged to process a first data set received at a first time.
  • the first computer 50 is arranged to perform a first part of the processing to identify substances present in a sample, such as pre-processing spectral data and/or the selection of peaks described above. Examples of processing that may be carried out by the first computer include charge deconvolution, de-isotoping and peak picking.
  • the first processor When the first processor has completed the first part of the processing that the first processor 50 is arranged to perform, the first processor outputs the result of the processing to a second processor 51 which is arranged to perform a second part of the processing, such as processing selected peaks to identify the set of substances ⁇ in the database to be further processed (for example based upon a shared peak count, as described above).
  • a second processor 51 which is arranged to perform a second part of the processing, such as processing selected peaks to identify the set of substances ⁇ in the database to be further processed (for example based upon a shared peak count, as described above).
  • the first processor Once the first processor has output to the second processor based upon the first data set, the first processor is arranged to perform the first part of the processing for a second data set.
  • a third processor is arranged to receive output from the second processor and to perform a further part of the processing, such as generating a value Score(k) for each substance in the set ⁇ .
  • each processor can be arranged to perform a particular function, and each processor can be optimised for the particular function.
  • the first processor 50 may be arranged to receive the signal and interpret the signal.
  • the interpreted signal may be output to the second processor 51 which is arranged to determine a set of substances based upon shared peak count.
  • the set out substances may be output to the third processor 52 which is arranged to generate a score P val (k) for each substance received from the second processors 51.
  • Table 1 shows the results from a computation on an equal 250fMol binary mixture of GLaD derivatised yeast Hexokinase (KIBYHA) and human ⁇ -lactalbumin (LAHU).
  • the column “Protein ID” shows proteins identified as significant hits
  • the column “Score” shows the value Score(k) determined according to the processing set out above
  • the column “nPeptides” shows the number of peptides in the identified protein
  • the column “MASCOT score” shows the score generated using the MASCOT MIM of Matrix Science Inc. on the same data multiplied by ten
  • the column “Rank” shows the rank of the proteins based upon Score(k).
  • Table 1 a number of almost identical proteins have the same score. The almost identical proteins are grouped together in Table 1. It can be seen that the order of substances based upon the value Score(k) matches the order of substances as determined by the MASCOT score closely. It can be seen that KIBYHA and LAHU are both correctly identified as present in the sample by MASCOT and by the value Score(k) having rank 1 and rank 3 respectively.
  • ALF_YEAST is a genuine contaminant of the sample and is correctly identified by both systems with rank 2.
  • Table 2 shows the value Score(k) for significant hits at different time intervals for a 50:50 mix of a Hexokinase (KIBYHA) and human ⁇ -lactalbumin (LAHU) sample run on an LC-IT-TOF (Liquid Chromatography - Ion Trap - Time of Flight) Mass Spectrometer. The sample is separated into fractions based upon retention time by the liquid chromatography column. The first column is the fraction in retention time minutes. There is a known contaminant fructose-bisphosphate aldolase (yeast) in the sample.
  • yeast contaminant fructose-bisphosphate aldolase
  • Hexokinase A ⁇ -lactalbumin and fructose-bisphosphate aldolase.
  • Hexokinase B is incorrectly identified it is a protein that is very similar to hexokinase A.
  • Table 3 shows the value Score(k) for significant hits at different time intervals for a 50:50 mix of Lysozyme (Chicken) and Glycerokinase (E.Coli) sample run on an LC-IT-TOF Mass Spectrometer. There is again a known contaminant fructose-bisphosphate aldolase (yeast) in the sample.
  • TABLE 3 Time Interval Significant Hits Score 26-29 LZCH - Lysozyme (chicken) 9.78
  • Other lysozyme related entries 8.57 29-32 No significant Hits - 32-35 GLPK_EC057 -Glycerol kinase (E. coli) 8.97 35"38 LZCH -Lysozyme (chicken) 13.74
  • the method correctly identifies Lysozyme and Glycerol kinase.
  • Table 2 and Table 3 were produced with a search using the correct taxonomies which allows increased search speed as less substances are required to be searched.

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Claims (17)

  1. Procédé de détermination d'une indication de la présence ou de l'absence d'une substance dans un échantillon comprenant :
    la réception de données générées à partir dudit échantillon, les données reçues comprenant une pluralité de points de données, chaque point de données ayant une valeur associée indicative d'une masse ou d'un rapport masse sur charge ;
    l'obtention de points de données théoriques associés à ladite substance, dans lequel chaque point de données théorique spécifie une valeur probabiliste pour une masse ou un rapport masse sur charge associé ;
    pour chacun de ladite pluralité de points de données, la détermination d'un score probabiliste, ledit score probabiliste étant déterminé sur la base d'un nombre de valeurs probabilistes des points de données théoriques ayant une valeur associée indicative d'une masse ou d'un rapport masse sur charge dans une plage de valeurs, ladite plage étant définie en relation avec ladite valeur associée du point de données ;
    le traitement desdits points de données théoriques et de ladite pluralité de points de données pour déterminer une correspondance entre lesdits points de données théoriques et ladite pluralité de points de données ;
    la détermination d'une force de ladite correspondance, ladite force de ladite correspondance étant basée sur lesdits scores probabilistes ; et
    la détermination de la présence ou de l'absence de ladite substance sur la base de ladite force de ladite correspondance.
  2. Procédé selon la revendication 1, dans lequel chaque point de données indique une abondance d'espèces chimiques ayant la masse ou le rapport masse sur charge associé.
  3. Procédé selon la revendication 1 ou 2, dans lequel, pour chacun de ladite pluralité de points de données, ladite plage est une plage centrée sur ladite valeur associée.
  4. Procédé selon la revendication 1, dans lequel, pour chacun desdits points de données, ledit score probabiliste est basé sur le nombre de points de données théoriques associés à ladite substance.
  5. Procédé selon l'une quelconque des revendications précédentes, dans lequel lesdits points de données théoriques sont associés à une catégorie de substances dont ladite substance est un élément.
  6. Procédé selon l'une quelconque des revendications précédentes, dans lequel le traitement desdits points de données théoriques et de ladite pluralité de points de données pour déterminer une correspondance entre lesdits points de données théoriques et ladite pluralité de points de données comprend :
    la détermination d'un compte de pics partagé, ledit compte de pics partagé indiquant un nombre de ladite pluralité de points de données ayant une valeur associée qui correspond à une valeur associée qui est associée à l'un desdits points de données théoriques.
  7. Procédé selon la revendication 6, dans lequel la détermination d'une force de correspondance comprend la génération d'un score probabiliste supplémentaire sur la base desdits scores probabilistes associés à chacun desdits points de données et dudit compte de pics partagé.
  8. Procédé selon la revendication 7, dans lequel la génération dudit score probabiliste supplémentaire comprend la multiplication desdits scores probabilistes associés à chacun desdits points de données.
  9. Procédé selon la revendication 8, comprenant en outre :
    l'énumération d'une pluralité de combinaisons ; et
    la sommation ou la multiplication desdits scores probabilistes sur la base desdites combinaisons énumérées,
    dans lequel l'énumération de ladite pluralité de combinaisons comprend en option :
    la mémorisation d'une première combinaison ; et l'exécution d'une ou de plusieurs opérations de décalage binaire pour énumérer d'autres combinaisons.
  10. Procédé selon l'une quelconque des revendications 7 à 9, dans lequel ledit score probabiliste supplémentaire indique une probabilité qu'un nombre de points de données théoriques indiqués par ledit compte de pics partagé aient des valeurs associées qui correspondent aux valeurs associées desdits points de données reçus ; ou
    dans lequel ledit score probabiliste supplémentaire indique une probabilité qu'un nombre de points de données théoriques supérieur ou égal audit compte de pics partagé par chance aient des valeurs associées qui correspondent aux valeurs associées desdits points de données reçus ; ou
    dans lequel ledit score probabiliste supplémentaire indique une probabilité qu'une première pluralité desdits points de données aient une valeur associée qui correspond à une valeur associée qui est associée à l'un desdits points de données théoriques et que les autres de ladite pluralité de points de données aient des valeurs associées qui ne correspondent pas à une valeur associée qui est associée à l'un desdits points de données théoriques.
  11. Procédé selon l'une quelconque des revendications précédentes, dans lequel lesdites données reçues sont des données spectrales, dans lequel chaque point de données dans lesdites données reçues est en option un pic dans lesdites données spectrales.
  12. Procédé selon l'une quelconque des revendications précédentes, dans lequel ledit échantillon comprend au moins une entité chimique sélectionnée dans le groupe consistant en des protéines, des peptides, des carbohydrates et des métabolites ; et/ou
    dans lequel ladite substance est sélectionnée dans le groupe consistant en des protéines, des peptides, des carbohydrates et des métabolites ; et/ou dans lequel lesdites données théoriques associées à ladite substance comprennent des données spectrales, dans lequel chaque point de données théorique est en option un pic dans lesdites données spectrales.
  13. Procédé de traitement d'un échantillon pour déterminer une indication de la présence ou de l'absence d'une substance dans ledit échantillon, ladite substance ayant des données théoriques associées, comprenant :
    la réception d'une première valeur de données indiquant un premier degré de correspondance entre lesdites données théoriques et des données générées à partir dudit échantillon à un premier instant, ladite première valeur de données étant déterminée conformément à l'une quelconque des revendications précédentes ;
    la réception d'une deuxième valeur de données indiquant un deuxième degré de correspondance entre lesdites données théoriques et des données générées à partir dudit échantillon à un deuxième instant, ladite deuxième valeur de données étant déterminée conformément à l'une quelconque des revendications précédentes ; et
    la détermination d'une indication de la présence ou de l'absence de ladite substance dans ledit échantillon sur la base dudit premier degré de correspondance et dudit deuxième degré de correspondance.
  14. Procédé selon la revendication 13,
    dans lequel la détermination d'une indication de la présence ou de l'absence de ladite substance dans ledit échantillon comprend la détermination que ladite substance est présente dans ledit échantillon si ladite première valeur de données et ladite deuxième valeur de données dépassent toutes deux un seuil prédéterminé ; et/ou
    dans lequel la détermination d'une indication de la présence ou de l'absence de ladite substance dans ledit échantillon est basée sur :
    une différence entre lesdites première et deuxième valeurs de données ; et/ou
    un taux de changement indiqué par lesdites première et deuxième valeurs de données.
  15. Procédé selon la revendication 13 ou 14, comprenant en outre :
    le traitement desdites première et deuxième valeurs de données pour déterminer s'il convient d'effectuer un traitement pour recevoir une troisième valeur de données indiquant un troisième degré de correspondance entre au moins l'un desdits points de données théoriques et des données générées à partir dudit échantillon à un troisième instant,
    dans lequel, en option, la détermination s'il convient d'effectuer un traitement pour recevoir une troisième valeur de données est basée sur :
    le taux de changement entre lesdites première et deuxième valeurs de données ; et/ou
    une relation entre lesdites première et deuxième valeurs de données et une valeur de seuil.
  16. Support pouvant être lu par un ordinateur supportant un programme d'ordinateur comprenant des instructions pouvant être lues par un ordinateur configurées pour amener un ordinateur à effectuer un procédé selon l'une quelconque des revendications 1 à 15.
  17. Appareil informatique pour déterminer la présence ou l'absence d'une substance dans un échantillon comprenant :
    une mémoire mémorisant des instructions pouvant être lues par un processeur ; et
    un processeur agencé pour lire et exécuter les instructions mémorisées dans ladite mémoire ;
    dans lequel lesdites instructions pouvant être lues par un processeur comprennent des instructions agencées pour commander l'ordinateur pour effectuer un procédé selon l'une quelconque des revendications 1 à 15.
EP10739968.5A 2009-07-28 2010-07-26 Traitement de données d'analyse spectrale Active EP2460175B1 (fr)

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PCT/GB2010/001413 WO2011012845A1 (fr) 2009-07-28 2010-07-26 Traitement de données d'analyse spectrale

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Citations (2)

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CA2620482A1 (fr) * 2007-02-07 2008-08-07 Thermo Finnigan Llc Analyse de donnees pour fournir un jeu de donnees revisees aux fins de sequencage de peptides
US7595485B1 (en) * 2007-02-07 2009-09-29 Thermo Finnigan Llc Data analysis to provide a revised data set for use in peptide sequencing determination

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BRUCE B. REINHOLD ET AL: "Electrospray ionization mass spectrometry: Deconvolution by an Entropy-Based algorithm", JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY., vol. 3, no. 3, 1 March 1992 (1992-03-01), US, pages 207 - 215, XP055303101, ISSN: 1044-0305, DOI: 10.1016/1044-0305(92)87004-I *

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WO2011012845A1 (fr) 2011-02-03
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