CN117368388A - Metabolic group chromatography peak extraction method based on secondary mass spectrum qualitative result - Google Patents
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- 238000001819 mass spectrum Methods 0.000 title claims abstract description 136
- 238000000605 extraction Methods 0.000 title claims abstract description 27
- 238000004587 chromatography analysis Methods 0.000 title description 2
- 230000002503 metabolic effect Effects 0.000 title description 2
- 238000001514 detection method Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 16
- 239000000126 substance Substances 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 150000002500 ions Chemical class 0.000 claims description 18
- 239000012634 fragment Substances 0.000 claims description 17
- 238000004949 mass spectrometry Methods 0.000 claims description 17
- 230000014759 maintenance of location Effects 0.000 claims description 11
- 230000003595 spectral effect Effects 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 7
- 150000001875 compounds Chemical class 0.000 claims description 4
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- 238000004451 qualitative analysis Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims 5
- 238000001228 spectrum Methods 0.000 claims 2
- 238000013480 data collection Methods 0.000 claims 1
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- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- DPJRMOMPQZCRJU-UHFFFAOYSA-M thiamine hydrochloride Chemical compound Cl.[Cl-].CC1=C(CCO)SC=[N+]1CC1=CN=C(C)N=C1N DPJRMOMPQZCRJU-UHFFFAOYSA-M 0.000 description 2
- 210000002700 urine Anatomy 0.000 description 2
- RGHMISIYKIHAJW-UHFFFAOYSA-N 3,4-dihydroxymandelic acid Chemical compound OC(=O)C(O)C1=CC=C(O)C(O)=C1 RGHMISIYKIHAJW-UHFFFAOYSA-N 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
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- 230000035764 nutrition Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 238000003068 pathway analysis Methods 0.000 description 1
- 210000002966 serum Anatomy 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
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Abstract
The invention discloses a metabonomics chromatographic peak extraction method based on a secondary mass spectrum qualitative result. The method starts from a secondary mass spectrum of metabonomics, and obtains more comprehensive chromatographic peak table information based on qualitative results of the secondary mass spectrum in a qualitative library and combining XCMS (XML Cryptographic Message Syntax) peak extraction results. The invention starts from the secondary mass spectrum characteristics which contain more mass spectrum information and have clear chemical substance meaning, and carries out the extraction of the chromatographic peaks of the metabonomics detection data, thereby improving the accuracy of the extraction of the chromatographic peaks, also defining the specific biological meaning of the metabonomics chromatographic peaks and being beneficial to the further discovery of metabonomics difference substances, the analysis of the passage and the interpretation of the biological meaning. Fig. 1 of the accompanying drawings is a flow chart of the present invention.
Description
Technical Field
The invention discloses a metabonomics chromatographic peak extraction method based on a secondary mass spectrum qualitative result, which belongs to the field of data science and is used for extracting chromatographic peaks from high-resolution mass spectrum detection data, improving the extraction accuracy of the chromatographic peaks and enhancing the biological significance of the chromatographic peaks.
Background
The metabonomics adopts a high-resolution detection instrument to detect the content of the metabolin in different types of samples (such as the early stage, the middle stage and the late stage of the disease), and the change rule analysis is carried out to excavate the change trend of the metabolin in the different types of samples, so that the metabonomics is an important component of the system biology, is widely applied to the fields including disease diagnosis, toxicology, botanic, nutrition food science, environmental science and the like, and has very wide application prospect.
However, there are still difficulties in the processing of metabonomic test data due to the complexity of the metabolites. For example, human serum is statistically more than 1,000 metabolites, and plants contain 4,000 to 25,000 metabolites. And is affected by background ions and random noise, so that accurate detection of chromatographic peaks containing metabolite features from metabonomics detection data is more challenging.
Currently, in metabonomics analysis, there are methods such as XCMS, MZmine, metAlign, openMS, which are commonly used methods for extracting chromatographic peaks from detection data based on primary mass spectrum information by adopting technologies such as chemometrics and the like from metabonomics data. These methods do not fully consider biological information of the compound contained in the chromatographic peak, and also do not fully utilize secondary mass spectrum information of detection data, so that false detection and omission of the chromatographic peak occur, and the subsequent results of metabonomic analysis operations such as qualitative identification, differential analysis and pathway analysis are affected.
Based on the method, the invention develops a metabonomics chromatographic peak extraction method based on a secondary mass spectrum qualitative result. Firstly, fully utilizing secondary mass spectrum information of detection data, and endowing chemical biological meaning to the secondary mass spectrum of the detection data by qualitatively identifying the secondary mass spectrum; and then, based on retention time and mass-to-charge ratio information of the secondary mass spectrum, finely positioning a chromatographic peak searching range in the primary mass spectrum data, improving the extraction accuracy of the chromatographic peak, simultaneously endowing the chromatographic peak with definite chemical biological meaning, and reducing false detection and omission of the chromatographic peak.
Disclosure of Invention
The invention aims to provide a metabonomics chromatographic peak extraction method based on a qualitative result of secondary mass spectrometry.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the metabonomics chromatographic peak extraction method based on the qualitative result of the secondary mass spectrum comprises the following steps:
(1) Step 1: extracting secondary mass spectrum data in metabonomics detection data, and carrying out denoising pretreatment on the secondary mass spectrum data.
Step 2: and screening the secondary mass spectrum data according to the similarity of the secondary mass spectrum data.
Step 3: based on the characteristics of the secondary mass spectrum, the qualitative analysis of the secondary mass spectrum is realized by combining a qualitative database, and the chemical meaning of the secondary mass spectrum is determined.
Step 4: and extracting chromatographic peaks based on the qualitative results of the secondary mass spectrum, and constructing a metabonomics detection data peak table.
Compared with the prior art, the invention develops a metabonomics chromatographic peak extraction method based on a secondary mass spectrum qualitative result, which has the following excellent effects: (1) the invention fully utilizes the secondary mass spectrum information to extract the chromatographic peak, shortens the searching range of the chromatographic peak, avoids searching the chromatographic peak in the whole retention time and the whole mass-to-charge ratio range, and improves the detection efficiency of the chromatographic peak; the occurrence of missed detection and false detection is avoided; (2) the invention fully utilizes the secondary mass spectrum information to extract the chromatographic peak, and determines the searching range of the chromatographic peak according to the retention time and the mass-to-charge ratio of the parent ion of the secondary mass spectrum, thereby avoiding the detection omission and the false detection of the chromatographic peak caused by factors such as system noise; (3) the chromatographic peak extracted by the method has full chemical biological meaning, the purpose of chromatographic peak extraction is to facilitate subsequent qualitative identification, differential analysis and path analysis of metabonomics, and further excavate and detect biological meaning in/among samples.
Drawings
Fig. 1 is a basic flow chart of the present invention.
FIG. 2 is an exemplary diagram of a screening result of a secondary mass spectrum.
FIG. 3 is an exemplary diagram of qualitative results of a secondary mass spectrum, wherein the upper (y > 0) region of the graph is the secondary mass spectrum to be characterized, and the lower (y < 0) region of the graph is the secondary mass spectrum in the qualitative library that matches it.
Fig. 4 is an ion profile in a determined chromatographic peak ROI area according to an example secondary mass spectrum.
Fig. 5 is a distribution of distances between ion mass-to-charge ratios and parent ion mass-to-charge ratios of a secondary mass spectrum in an ROI region of a chromatographic peak corresponding to the secondary mass spectrum.
Fig. 6 is a chromatogram peak of final extraction based on an example secondary mass spectrum.
Detailed Description
The invention is described in further detail below with reference to the attached drawings, wherein:
example data: for 40 mice divided into a normal group (5 mice), a model group (8 mice), a positive group (6 mice), a low dose group (7 mice), a medium dose group (7 mice) and a high dose group (7 mice), and 40×4=160 mice urine samples collected at 4 stages of day 0, day 10, day 20 and day 30 are respectively measured and mixed by 160 decimal urine samples and the like to obtain 16 QC samples (160+16=176 experimental samples in total), and the detection experiment is carried out by adopting a SCIEX 7600 instrument positive ion ionization mode to obtain corresponding 176 mass spectrum detection data.
The classification of 176 mass spectrum data obtained was examined.
Table 1176 classification of mass spectrometry detection data
From the 176 detected mass spectrum data, 282,436 secondary mass spectra were extracted and automatically analyzed for a noise intensity level of 155.152.
After the deduplication treatment, 172,734 secondary mass spectra were finally selected for characterization.
172,734 secondary mass spectra are subjected to qualitative analysis in qualitative libraries such as HMDB and MoNA, and 16,922 secondary mass spectra with qualitative results are finally obtained.
Of the 16,922 secondary mass spectra with qualitative results, one of the secondary mass spectra with a qualitative result of DI-3,4-Dihydroxymandelic acid (DL-3, 4 dihydroxymandelic acid) was selected as an exemplary secondary mass spectrum (the retention time of the selected exemplary secondary mass spectrum was 13.00min, the mass-to-charge ratio was 149.0235 Da), and how to extract chromatographic peaks from data according to the secondary mass spectrum in the metabonomics chromatographic peak extraction method based on qualitative results of the secondary mass spectrum of the present invention was described in detail.
A plot of the secondary mass spectral fragments and their noise levels selected as an example secondary mass spectrum is shown in fig. 2.
The plot of the matching of the secondary mass spectrum selected as an exemplary secondary mass spectrum with DI-3,4-Dihydroxymandelic acid fragments is shown in fig. 3, the upper (y < 0) region of the graph in fig. 3 is a plot of the secondary mass spectrum fragments of the exemplary secondary mass spectrum, and the lower (y < 0) region of the graph is a plot of the secondary mass spectrum fragments of DI-3,4-Dihydroxymandelic acid compounds, both of which have their fragment intensities normalized for the convenience of observing the comparison of both.
Based on the selected exemplary secondary mass spectrum, a region having a retention time in the range of [13.00-0.5,13.00+0.5] and a mass-to-charge ratio in the range of [149.0235-0.015, 149.0235+0.015] is set as the ROI region of the chromatographic peak corresponding to the exemplary secondary mass spectrum in the detected mass spectrum data according to the retention time and the mass-to-charge ratio thereof. The mass spectrum ion distribution in this region is shown in fig. 4.
The absolute values of the differences between the mass-to-charge ratios of all mass spectral ions in the ROI area and 149.0235 are calculated, and the distribution of the absolute values of the differences is shown in fig. 5.
After the absolute values of the differences between the mass-to-charge ratios of all mass spectrum ions in the ROI area and 149.0235 are clustered, all mass spectrum ions in the cluster with the absolute values of the differences being less than or equal to 0.003 are selected, and chromatographic peaks are drawn as shown in figure 6.
According to the steps, chromatographic peaks and peak areas thereof in 176 mass spectrum data corresponding to an example secondary mass spectrum are obtained.
According to the steps, 16,922 chromatographic peaks and peak areas of the secondary mass spectrum with qualitative results in 176 mass spectrum data are obtained.
Finally, according to the metabonomics chromatographic peak extraction method based on the secondary mass spectrum qualitative result, 16,922 chromatographic peaks with definite chemical and biological significance and obvious peak type are obtained in 176 mass spectrum data.
The same 176 mass spectrum data are shown in the following table, comparing the number of chromatographic peaks extracted by the metabonomics chromatographic peak extraction method based on the qualitative results of the secondary mass spectrum of the present invention with the number of chromatographic peaks obtained after the chromatographic peak extraction by XCMS, MZmine, metAlign, openMS.
TABLE 2 comparison of the number of chromatographic peaks extracted by different methods
As shown in the table above, the chromatographic peak extracted by the chromatographic peak extraction method provided by the invention has clear chemical and biological significance, and the number of the chromatographic peak extraction is obviously increased compared with that of the chromatographic peak extraction methods such as XCMS (71.94% increase), MZmin (61.90% increase), metAlign (99.27% increase), openMS (27.29% increase) and the like, so that the omission factor of the chromatographic peak extraction is greatly reduced.
Claims (5)
1. A metabonomics chromatographic peak extraction method based on a secondary mass spectrum qualitative result is characterized in that the qualitative result of the secondary mass spectrum is adopted as a chromatographic peak extraction basis, so that the number of metabonomics chromatographic peaks is increased, the specific biological significance of the metabonomics chromatographic peaks is also clarified, and further metabonomics difference discovery, path analysis and biological significance interpretation are facilitated. Comprising the following steps:
step 1: extracting secondary mass spectrum data in metabonomics detection data, and carrying out denoising pretreatment on the secondary mass spectrum data.
Step 2: and screening the secondary mass spectrum data according to the similarity of the secondary mass spectrum data.
Step 3: based on the characteristics of the secondary mass spectrum, the qualitative analysis of the secondary mass spectrum is realized by combining a qualitative database, and the chemical meaning of the secondary mass spectrum is determined.
Step 4: and extracting chromatographic peaks based on the qualitative results of the secondary mass spectrum, and constructing a metabonomics detection data peak table.
2. The metabonomics detection data secondary mass spectrum data denoising pretreatment method according to claim 1, wherein the specific method of step 1 is as follows:
important features of a metabonomic secondary mass spectrometry data can be represented as data structuresWherein tR 1 Parent ion retention time, mz, representing secondary mass spectra 1 Represents the mass-to-charge ratio of the parent ion of the secondary mass spectrum,a set of m secondary mass spectral fragments representing secondary mass spectral data, +.>And->The mass to charge ratio and intensity of the ith secondary mass spectral fragment are shown, respectively.
In the invention, the process of screening the secondary mass spectrum data is as follows:
firstly, denoising all secondary mass spectra without losing generality, and performing spec on ith secondary mass spectrum data i During pretreatment, finding spec i The secondary mass spectrum with highest medium intensity is named spec i :Then for specs according to the following method in turn i J-th secondary mass spectral feature +.>Processing ifIf any one of the following conditions (1), (2) and (3) is satisfied, it is considered thatIs a noise secondary mass spectrum characteristic and needs to be removed; otherwise, the secondary mass spectrum characteristic is considered to be qualified secondary spectrum characteristic, and the secondary spectrum characteristic can be reserved for the next processing.
①
②it_thred1 is a preset secondary mass spectrum characteristic intensity threshold;
(3) at the position ofWithin the range, spec i There are multiple secondary mass spectral features, and +.>Of these secondary mass spectral fragments, the intensity is not highest.
3. The secondary mass spectrum screening method according to claim 1, wherein the specific method in step 2 is as follows:
defining two secondary mass spectrometry data specs A And spec B The similarity calculation flow of (2) is as follows:
(1) if |spec A :tR 1 -spec B :tR 1 |＞tr_thred_2，spec A And pec B The similarity value of (2) is 0, and the spec is completed A And spec B If not, skipping to (2);
(2) if |spec A :mz 1 -spec B :mz 1 |＞mz_thred_2，spec A And pec B The similarity value of (2) is 0, and the spec is completed A And spec B If not, skipping to (3);
(3) statistical spec A M in A Secondary mass spectral features and pec B M in B Of the secondary mass spectrum characteristics, the secondary mass spectrum has a mass-to-charge ratio distance of less than a threshold mz2_thred_2, and the number m of the secondary mass spectrum characteristics is less than the threshold mz2_thred_2 A∩B Then the second-level mass spectrum data spec A And spec B Similarity simi of (2) A,B ＝(2×m A ∩ B )/(m A +m B )。
Based on the secondary mass spectrum data similarity calculation flow, all noise-removed secondary mass spectrum data { spec ] to be screened are subjected to 1 ,spec 2 ,…,spec n Sorting according to the number of secondary mass spectrometry features, and screening secondary mass spectrometry data according to the following steps.
(1) Initializing i=1, and eliminating a secondary mass spectrum set P=phi;
(2) thereby calculating spec i And spec j Similarity between j E { i+1, i+2, …, n } is simi i,j If simi i,j > simi_thred_2, then spec will be j Placing the mass spectrum into a secondary mass spectrum set P to be removed;
(3) setting i to i+1, and performing (2) circularly until i=n;
(4) from the secondary mass spectrum data { spec } a secondary mass spectrum in the secondary mass spectrum set P 1 ,spec 2 ,…,spec n And (3) removing the secondary mass spectrum data from the collection, wherein the rest secondary mass spectrum data are the secondary mass spectrum data collection after screening.
4. The qualitative method according to claim 1, wherein the specific method of step 3 is as follows:
the qualitative of the secondary mass spectrum data to be qualitative is based on the matching degree calculation result between the secondary mass spectrum data and the secondary mass spectrum characteristics of the known compound substances in the qualitative library, and the secondary mass spectrum qualitative to be qualitative is determinedAs a result, a secondary mass spectrum spec to be characterized needs to be calculated A And all secondary mass spectra { spec ] in qualitative libraries 1 ,spec 2 ,…,spec n Performing matching degree calculation on the secondary mass spectrum to be qualitatively detected(wherein S A For Spec A Is a set of secondary mass spectrometry fragments,secondary mass spectrum in qualitative library(wherein S B For Spec B Is a set of secondary mass spectrometry fragments,the matching degree of the two is calculated as follows.
(1) According to S A Obtaining Spec A Mass to charge ratio collection of secondary mass spectrometry fragmentsAccording to S B Obtaining Spec B Mass-to-charge ratio set of secondary mass spectrometry fragments +.>
(2) For collectionsAll->If->Not in the aggregate->In, then second order Mass Spectrometry fragment +.>Added to S A Finally obtaining a secondary mass spectrum fragment set S' A ；
(3) For collectionsAll->If->Not in the aggregate->In, then second order Mass Spectrometry fragment +.>Added to S B Finally obtaining a secondary mass spectrum fragment set S' B ；
(4) A second-order mass spectrum fragment set S 'through the treatment of the step (2) and the step (3)' A And a second mass spectrometry fragmentation set S' B The mass-to-charge ratios of all the secondary mass spectral fragments are in one-to-one correspondence. Sequencing according to the mass-to-charge ratio value from small to large to obtain a mass-to-charge ratio sequenceMass to charge ratio sequence->In S' A The corresponding intensity sequences in (a) are recorded asMass to charge ratio sequence->In S' A The corresponding intensity sequences in (a) are recorded as
(5) Calculation ofAnd->The Person correlation of the two data sequences is calculated as shown in the following formula.
According to the five steps (1) (2) (3) (4) (5), calculating the secondary mass spectrum spec to be qualified A And all secondary mass spectra { spec ] in qualitative libraries 1 ,spec 2 ,…,spec n Match degree of the second-order mass spectrum spec with undetermined degree A The maximum value of the matching degree with all the secondary mass spectra in the qualitative library is r max The secondary mass spectrum in the corresponding qualitative library is Spec max . If r max ≥r thred Wherein r is thred For a preset qualitative threshold, spec in qualitative library max Chemical information such as corresponding compound name, molecular formula, molecular weight and the like is undetermined secondary mass spectrum spec A Qualitative results of (2) completing the secondary mass spectrometry spec A Is characterized by (1); otherwise, second-order mass spectrometry spec A Qualitative failure, i.e. secondary mass spectrometry spec A Is not meant to represent a particular compound.
According to the steps, the qualitative of all secondary mass spectra to be qualitative is finished, and according to whether the qualitative is successful, all secondary mass spectra to be qualitative are divided into secondary mass spectra with qualitative results and secondary mass spectra without successful qualitative.
5. The chromatographic peak extraction method according to claim 1, wherein the specific method of step 4 is as follows:
after passing the qualitative, the second-level mass spectrum data spec with qualitative results i E D (where D is the secondary mass spectrum data set with qualitative results), according to spec i i=1, 2, …, and extracting chromatographic peaks from the original data.
In the invention, the specific implementation steps of chromatographic peak extraction based on the secondary mass spectrum information are as follows:
(1) spec for secondary mass spectrometry i i=1, 2, …, i D i, its retention time is recorded as spec i :tR 1 Its mass-to-charge ratio is indicated as spec i :mz 1 In the raw data raw_data, according to the retention time range [ spec ] i :tR 1 -ΔtR,spec i :tR 1 +ΔtR]Mass-to-charge ratio range [ spec i :mz 1 -Δmz,spec i :mz 1 +Δmz]The area ROI (Region of Interest) is delineated. Wherein Δtr is a preset retention time range threshold and Δmz is a preset mass-to-charge ratio range threshold.
(2) In the original data, the characteristic ion sets in the region ROI are marked as E, and the characteristic ion ions in the E are calculated respectively j (j=1, 2, …, i E i) mass-to-charge ratio spc i :mz 1 With a secondary mass spectrum spec i Mass to charge ratio spec i :mz 1 Distance d of (2) ij ：
d ij ＝|spc i :mz 1 -ion j :mz 1 |
(3) Based on characteristic ions and secondary mass spectrum spec in ROI i The characteristic ions are clustered by adopting an optimal k-means clustering method, and the clustering result is marked as Cluster 1 ,Cluster 2 ,…,Cluster k . In k clustering results (clusters), secondary mass spectrum specs are recorded i Mass to charge ratio spec i :mz 1 The Cluster with the smallest average distance is Cluster min 。
④Cluster min All characteristic ions in the sample are sequenced according to the retention time, and the peak formed by connecting the characteristic ions and smoothing the characteristic ions through first-order derivation is the second-level mass spectrum spec i The chromatographic peak corresponding to the E-D is obtained to obtain the peak area of the chromatographic peak, namely the second-level mass spectrum spec i E the chromatographic peak area of D in the sample.
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