CN106018600A - Metabolism group method for distinguishing false positive mass spectra peak signals and quantificationally correcting mass spectra peak area - Google Patents
Metabolism group method for distinguishing false positive mass spectra peak signals and quantificationally correcting mass spectra peak area Download PDFInfo
- Publication number
- CN106018600A CN106018600A CN201610346054.2A CN201610346054A CN106018600A CN 106018600 A CN106018600 A CN 106018600A CN 201610346054 A CN201610346054 A CN 201610346054A CN 106018600 A CN106018600 A CN 106018600A
- Authority
- CN
- China
- Prior art keywords
- sample
- peak
- mix
- metabolite
- concentration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01N2030/022—Column chromatography characterised by the kind of separation mechanism
- G01N2030/027—Liquid chromatography
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a metabolism group method for distinguishing false positive mass spectra peak signals and quantificationally correcting mass spectra peak area. The invention provides a metabolism group study method; by the method, the biological source and non-biological source mass spectra peak signals can be effectively distinguished; the mass spectra peak signals are quantificationally evaluated; the mass spectra peaks with poor quantification performance are excluded; through QC sample dilution, a relative content correction model is built; the mass spectra peak area is corrected. The method has the maximum characteristics that the false positive mass spectra signals can be effectively eliminated, so that the metabolism group data becomes reliable; the real biomarkers can be favorably screened. The method can aim at plant, animal and microbe samples; the method can also be suitable for the mass-spectra-platform-based metabolism group analysis of GC-MS, LC-MS and CE-MS.
Description
Technical field
The present invention relates to biological technical field, particularly relate to a kind of differentiation false positive mass spectra peak signal and quantitative correction mass spectrum
The metabolism group method of peak area.
Background technology
Along with the mensuration of mankind's whole genome sequence, the various groups of concepts learned are arisen at the historic moment.Genomics research drives
The fast development of life sciences, and greatly promoted transcription group, proteomics, metabolism group, phenomics
Deng fast development.Metabolism group (Metabonomics/Metabolomics) is intended to postgraduate's object or organizes very
Whole small molecule metabolites compositions and dynamic change (Nicholson JK andLindon thereof to individual cells
JC.Nature 455:1054–6).Metabolite is closest away from phenotype, is the bridge between gene and phenotype.Metabolism group
Be a very important link in systems biology research, is possible not only to disclose the function of gene, is also biological skill
The application of art provides scientific basis (Chen, W., et al., Nat Genet.46:714-721；Sreekumar,A.,
et al.,Nature 457:910-914)。
The various high sensitivity of the demand promotion of metabolism group research, the developing rapidly of Analytical high resolution instrument.At present,
Metabolism group mainly uses two big analytical technology platforms, nuclear magnetic resoance spectrum (Nuclear Magnetic Resonance,
NMR) platform and mass spectrum (Mass Spectrometry, MS) platform.Although NMR have simple sample pretreatment,
The advantages such as higher repeatability and good detection objectivity, but mass spectrum has higher resolution and sensitivity, right
The sample so complicated in plant is especially suitable for (Antignac, JP., et al., TrAC-Trend Anal.Chem.
30:292-301；De Vos,RCH.,et al.,Nat Protoc.2:778-791).
Metabolism group makes every effort to analyze all of small molecule metabolites, it is desirable to analytical tool can detect metabolism as much as possible
Thing.But, the mixture owing to chromatograph-mass spectrometer coupling technology has the highest sensitivity, to 20 kinds of standard substance compositions
Thousands of kinds of mass signal, the false positive more than 90% can be extracted.Some false positive signal has randomness, containing of they
Amount fluctuation can directly affect statistical analysis below higher than the difference of biogenetic derivation metabolite content most of the time,
Greatly interfere with the screening of true biomarker " biomarkers ".Lacking the metabolism group of data quality control
In analysis, often can not find real biological marker, or screen false positive biomarkers (Broadhurst,
DI.and Kell,DB..Metabolomics 2:171-196；Kim,S.and Zhang,X..J Chemometr
29:80-86；Redestig,H.et al.,Anal.Chem.83:5645-5651).Divide in metabolism group data
, there is contradiction detecting as far as possible between more signal and minimizing false positive signal, how to filter mass spectrometric data and do not have in analysis
There is unified standard.And the change that mass spectrometric data processes one small parameter of software makes the quantity at metabolite peak occur huge
How big change, evaluate these signals, how to accept or reject these signals, also lacks rational logic judging method at present.
The mass signal of chromatograph-mass spectrometer coupling metabolism group method can be divided into two kinds, and a kind of is biogenetic derivation, a kind of
Non-biological origin, i.e. solvent impurity, chromatographic column, instrument pipeline, the extraneous contamination that experiment consumptive material brings.Generally
One dummy can obtain thousands of mass spectra peak signals easily, and a biological sample can obtain thousands of mass spectrum
Peak-to-peak signal, the signal of non-biological origin account for major part.For the signal of biogenetic derivation, be not all of signal all
It is suitable for metabolism group quantitative analysis, the component that such as those content are high, extremely low, can obtain in Spectrometry
Signal, but it is not necessarily present or exists the dependency of extreme difference between their signal intensity and actual concentration.Directly make
By mass spectra peak Area comparison relative quantification relation, often obtain the result of mistake.Therefore, non-biological origin is distinguished
Mass spectra peak signal, the mass spectra peak signal evaluating biogenetic derivation has important meaning to metabonomic analysis.
The method that the method for the mass spectra peak signal that can effectively distinguish non-biological origin at present is mainly isotope labelling metabolism group,
The method needs to use C13, H2Deng cold labeling whole metabolism group (Giavalisco, P.et al., Anal
Chem81:6546-51), judge that those mass spectra peaks are derived from organism by mass spectrum displacement, it is possible to mass spectrum
The number of offset qualification carbon atom (or hydrogen atom), carries out qualitative analysis to mass spectra peak signal.Isotope labelling metabolism
Prescription method is highly effective, but cost is the highest, and technology requires the highest.The metabonomic analysis methods of targeting, with mark
Quasi-product, analyze the content difference of known metabolite, and these can be confined to analyze certain or some concrete metabolism with method
Thing (Phinney, KW.et al. .Anal Chem 85:11732-11738), and for non-targeted metabolism group
Test improper.
Summary of the invention
A kind of method that it is an object of the present invention to provide difference metabolite distinguishing different sample.
The method that the present invention provides, comprises the steps:
1), preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample, multiple sample to be tested metabolite sample；
Described QC_mix sample is to be mixed by the metabolite solution of multiple samples to be tested, obtains QC_mix sample；
Described metabolite solution is to be made up of metabolite and organic solvent or with organic solvent extraction sample to be tested
Metabolite obtains, as sample to be tested metabolite sample；
Described solvent blank sample is made up of internal standard and described organic solvent；
Described different extension rate QC_mix sample for carrying out stepwise dilution, the difference obtained to described QC_mix sample
Extension rate QC_mix sample；
Described different cycles of concentration QC_mix sample for described QC_mix sample is concentrated step by step, the difference obtained
Cycles of concentration QC_mix sample；
The plurality of sample to be tested metabolite sample is the metabolite solution of multiple sample to be tested；
2), chromatograph-mass spectrometer coupling detecting step 1 successively) each sample of obtaining, described detection comprises the steps:
(1) carry out described solvent blank sample, more than or equal to 3 detections, obtaining being more than of solvent blank sample
In the raw mass spectrum data of 3 times；
(2) described QC_mix sample is carried out more than or equal to 6 times detection, obtain QC_mix sample more than or equal to 6
Secondary raw mass spectrum data；
(3) to described different extension rate QC_mix samples and different cycles of concentration QC_mix samples according to concentration from
Little detect successively 1 time to big order, obtain different extension rate QC_mix samples that concentration arranges from small to large and not
Raw mass spectrum data with cycles of concentration QC_mix sample；
(4) the plurality of sample to be tested metabolite sample is carried out respectively 1 detection, obtains multiple sample to be tested
The raw mass spectrum data of metabolite sample；
3), to step 2) all mass spectrometric datas of obtaining carry out peak and extract and peak aligns, and obtain the peak row of all samples
Table matrix；The false positive in described peak list matrix is filtered again with the five step mass spectra peak filtering rules of following (A)-(E)
Peak, mass spectra peak after being filtered:
(A) choose QC_mix sample in described peak list matrix more than or equal to the peak frequency of occurrences in 6 testing results >
Peak equal to 80% retains；
(B) choose in (A) processes the peak that obtains QC_mix sample more than or equal to 6 testing results in peak variation
Property RSD < 20% peak retain；
(C) choose the B/S value peak less than 5% in (B) processes the peak obtained to retain；
B/S value is more than than QC_mix sample more than or equal to 3 testing result peak area meansigma methodss for solvent blank sample
In 6 testing result peak area meansigma methodss；
(D) QC_mix difference extension rate sample concentration times different with QC_mix in (C) processes the peak obtained is calculated
The pearson correlation coefficient r of the relative concentration index that the peak area of numerical example is corresponding with each sample, chooses 0.7 < r < 0.99
Peak retain, it is achieved the elimination of false positive mass spectra peak；
The relative concentration index that described QC_mix difference extension rate sample cycles of concentration sample different with QC_mix is corresponding
Obtain according to the method that comprises the steps as follows: by described QC_mix difference extension rate sample and described QC_mix not
Giving relative concentration index with the sample that concentration in cycles of concentration sample is minimum is X, calculates remaining according to equation 1 below dense
The relative concentration index y of degree sample, obtains the relative of all QC_mix difference extension rate cycles of concentration different with QC_mix
CI；
Formula 1: sample relative concentration this sample of index y=is relative to the concentration multiple * X of concentration smallest sample
X is the numerical value of 0；
(E) mass spectra peak processing obtain 0.7 < r < 0.99 through (D) is carried out the number of manual correction alignment integration mistake
Strong point, it is achieved eliminate false positive mass spectra peak.
4), by step 3) the described filtration that obtains of E after the peak area of mass spectra peak and the relative concentration index of its correspondence build
Vertical regression model；Again the mass spectrum peak area of the metabolite of each described sample to be tested is substituted into regression model respectively to enter
Row correction and normalization, obtain the relative concentration index of each sample to be tested；
Described regression model includes linear regression model (LRM), binomial regression model, logistic regression models, index return mould
Type and/or two-pass regression model；
5), the relative concentration index of all samples to be tested is carried out multivariate or univariate statistics analysis, find multiple treating
Test sample difference metabolite originally.
In said method, step 2) in, described chromatograph-mass spectrometer coupling is GC-MS, LC-MS and CE-MS；
And/or, described chromatograph-mass spectrometer coupling is specially LC-MS；
Step 3) in, the software that described peak extracts and peak alignment uses is mass spectrum Preprocessing software,
And/or, described mass spectrum Preprocessing software is specially xcms or Masshunter Quanlitative
Analysis；
Step 3) E in, the mass spectra peak processing obtain 0.9 < r < 0.99 through (D) is carried out manual correction alignment long-pending
Misclassification data point by mistake.
In said method, step 1) in, described sample to be tested is biogenetic derivation sample and/or non-biological origin sample；
Described biogenetic derivation sample is specially plant, animal and/or microorganism.
In said method, step 1) in, the plurality of sample to be tested number is more than or equal to 2；
Described organic solvent is methanol,
Umbelliferon it is designated as in described；
Step 2) in, described QC_mix sample is to be uniformly mixed so as to obtain by the metabolite solution equal-volume of multiple samples to be tested；
The diluent that described dilution uses is described organic solvent；
Described stepwise dilution is that stepwise dilution is to 16-32 times；
Described concentration step by step is for be concentrated into 2-4 times step by step.
Another object of the present invention is to provide false positive mass spectra peak removing method in a kind of metabolite detection.
The method that the present invention provides, comprises the steps: as the step 1 in above-mentioned)-3), it is achieved false positive mass spectra peak
Eliminate.
The application in the metabolite distinguishing different sample of the above-mentioned method is also the scope of protection of the invention.
The application in the metabolite of quantitative correction difference sample of the above-mentioned method is also the scope of protection of the invention.
The application in false positive mass spectra peak signal in distinguishing sample of above-mentioned method is also the scope of protection of the invention.
The condition of above-mentioned LC-MS is as follows:
(1) chromatographic condition: chromatographic apparatus, Agilent company 1290 type ultrahigh pressure liquid phase chromatograph (UHPLC), chromatograph
Post ZORBAX Eclipse Plus C18 (Agilent), 2.1*100mm, packing material size 1.8 μm.
(2) sample size 5ul, column temperature 30 degree, phase composition of flowing: A water (0.1%, formic acid), B acetonitrile (0.1%,
Formic acid), UHPLC gradient elution program is as follows: initial 5%B, 5min → 20%B, 15min → 40%B, 25min
→ 100%B, 28 → 100%B, 28.5 → 5%B balance 2.5min, whole elution time 30min.
(3) Mass Spectrometry Conditions: chromatographic apparatus, Agilent company 6540 type quadrupole rod time-of-flight mass spectrometry
(Q-TOF/MS), it is equipped with high sensitivity and sprays sub-focusedion source (Jet stream ESI) of wandering about as a refugee.Full scan pattern
(full scan), mass range 50-1000, acquisition rate, 2spectrum/s, correction ion under positive ion mode:
M/z 121.050873,922.009798.Positive ion mode parameter is arranged: carrier gas temperature, 350 DEG C；Dry gas stream
Speed: 8L/min；Nebulizer pressure, 35psi；Sheath temperature, 350 DEG C；Sheath gas velocity, 8L/min；V cap voltage,
4000V；Fragmentor voltage, 130V；Nozzle voltage, 0V.Negative ion mode parameter is arranged: carrier gas temperature
Degree, 350 DEG C；Dry gas stream speed: 8L/min；Nebulizer pressure, 35psi；Sheath temperature, 350 DEG C；Sheath gas velocity,
8L/min；V cap voltage, 3000V；Fragmentor voltage, 130V；Nozzle voltage, 1000V.
The present invention provides simply, low cost, effectively metabolism group New Research Method, and the method can effectively distinguish life
Thing source and the mass spectra peak signal of non-biological origin, and mass spectra peak signal is carried out quantitative assessment, get rid of quantitation capabilities
The mass spectra peak that (Quantification Performance) is poor.Set up relative amount by QC Sample Dilution to correct
Model, is corrected mass spectra peak area.The feature of the method maximum is effectively to eliminate false-positive mass spectra peak signal,
Make metabolism group data become reliable, be conducive to screening real metabolism marker (Biomarkers).This method is permissible
For plant, animal, micro-biological samples, it is also possible to be suitable for the generation based on mass spectrometric platforms of GC-MS, LC-MS and CE-MS
Thank to group credit analysis.
Compared with prior art, the outstanding advantages having is to establish a kind of simple, low cost, effectively area to the present invention
Decomposing biological source and the mass spectra peak signal of non-biological origin, get rid of the mass spectra peak that quantitation capabilities is poor, to mass spectra peak area
It is corrected, improves the reliability of metabolism group data.
(1) simple, low cost.Compared with isotope-labeled method, the present invention need not extra isotope mark
Note technology and instrument, it is not required that expensive isotope marker material.Any metabolism group laboratory all can complete, only
Need carry out the preparation of sample and carry out according to the detection mode of the present invention.
(2) false positive signal and the signal of quantitation capabilities difference are efficiently differentiated.By five step mass spectra peak filtering rules,
Can greatly reduce mass spectra peak quantity, this five steps mass spectra peak filters from the angle of analytical chemistry, the repeatability i.e. analyzed,
The accuracy of detection, several aspect such as blank and quantitative correlation comprehensively analyzes, and these parameters have logicality,
Avoid artificially filtering mass spectra peak arbitrarily.
(3) introduce relative concentration index concept, be possible not only to the quantitative performance at peak is carried out quantitatively evaluating, but also
Relative correction model can be set up, mass spectra peak area is corrected, improves dosing accuracy.
(4) high flux, mass processes.By QC_mix is analyzed, all of metabolite is carried out together
Assay, and all compounds can be set up relative regression curve by a dilution experiment, the most cost-effective
And workload.
(5) by the invention it is possible to non-targeted metabolism group method to be converted into the metabonomic analysis methods of targeting.
(6) analysis strategy that the present invention uses, including mixing each biological specimen composition Quality Control sample (Quality
Control, QC), Quality Control sample is diluted, solvent blank sample etc. is set.And invention provides for loading
Sequentially, five step peak filtering rules etc..The analog sample that standard substance are formed by this method, it is possible to eliminate the false sun of 92.4%
Character spectral peak signal, can eliminate the false positive mass signal of 71.4% to the biological sample of rice grain.This method letter
List, convenience, low cost, elimination false positive effect are preferable, it is ensured that the quality of data of metabolism group.
Accompanying drawing explanation
Fig. 1 is to distinguish false positive mass spectra peak signal, the metabolism group new method flow chart of raising dosing accuracy.
Fig. 2 is that QC_mix dilutes for distinguishing false positive mass spectra peak signal.
Fig. 3 is artificial sample compound method, various criterion product concentration ratio in 12 groups of artificial samples.
Fig. 4 is five step filtering rules, and artificial sample reduces the effect of false positive mass spectra peak.
Fig. 5 is relative concentration index and relative concentration calibration model.
Fig. 6 is step filtering rule, and rice grain sample reduces the effect of false positive mass spectra peak.
Fig. 7 is that this method (right) is distinguishing two kinds of rice-cultivating seeds with tradition metabolism group method (left), sets up
Difference in principal component model.
Fig. 8 is that this method (right) is distinguishing rice grain principal component analysis score with tradition metabolism group method (left)
Difference in figure.
Fig. 9 be this method (under) with tradition metabolism group method (on) distinguish rice grain principal component analysis load
Difference in figure.
Figure 10 is this method (yellow) and tradition metabolism group method (blue) two kinds of rice grain difference metabolite of screening
Quantitative difference.
Detailed description of the invention
Experimental technique used in following embodiment if no special instructions, is conventional method.
Material used in following embodiment, reagent etc., if no special instructions, the most commercially obtain.
The present invention will be further described below in conjunction with the accompanying drawings.
Embodiment 1, the present invention are for distinguishing false positive mass spectra peak signal and the metabolism group side of quantitative correction mass spectra peak area
Method
One, false positive mass spectra peak removing method in metabolite detection, comprises the steps:
1, preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample, multiple sample to be tested metabolite sample；
QC_mix sample is to be mixed by the metabolite solution of multiple samples to be tested, obtains QC_mix sample；
Metabolite solution is to be made up of metabolite and organic solvent or with the metabolism of organic solvent extraction sample to be tested
Product obtains, as sample to be tested metabolite sample；
Solvent blank sample is made up of internal standard and organic solvent；
Different extension rate QC_mix samples for carrying out stepwise dilution, the different extension rates obtained to QC_mix sample
QC_mix sample；
Different cycles of concentration QC_mix samples for QC_mix sample is concentrated step by step, the different cycles of concentration obtained
QC_mix sample；
2, chromatograph-mass spectrometer coupling detecting step 1 successively) each sample of obtaining, detection comprises the steps:
(1) solvent blank sample is carried out more than or equal to 3 detections to completely overlapped (the i.e. instrument of total ions chromatogram
Device reaches steady statue), obtain the raw mass spectrum data more than or equal to 3 times of solvent blank sample；
(2) QC_mix sample is carried out more than or equal to 6 times detection, obtain QC_mix sample more than or equal to 6 times
Raw mass spectrum data；
(3) to different extension rate QC_mix samples and different cycles of concentration QC_mix samples according to concentration from little to
Big order detects 1 time successively, obtains different extension rate QC_mix samples that concentration arranges from small to large with the denseest
The raw mass spectrum data of demagnification number QC_mix sample；
(4) multiple sample to be tested metabolite samples are carried out respectively 1 detection, obtains multiple sample to be tested metabolism
The raw mass spectrum data of product sample；
3, all mass spectrometric datas obtaining step 2 carry out peak extraction and peak alignment, obtain the peak list square of all samples
Battle array；Filter the false positive peak in peak list matrix with the five step mass spectra peak filtering rules of following (A)-(E) again, obtain
Mass spectra peak after filtration:
(A) choose QC_mix sample in peak list matrix more than or equal to the peak frequency of occurrences in 6 testing results > be equal to
The peak of 80% retains；
(B) choose in (A) processes the peak that obtains QC_mix sample more than or equal to 6 testing results in peak variation
Property RSD < 20% peak retain；
(C) choose the B/S value peak less than 5% in (B) processes the peak obtained to retain；
B/S value is more than than QC_mix sample more than or equal to 3 testing result peak area meansigma methodss for solvent blank sample
In 6 testing result peak area meansigma methodss；
(D) QC_mix difference extension rate sample concentration times different with QC_mix in (C) processes the peak obtained is calculated
The pearson correlation coefficient r of the relative concentration index that the peak area of numerical example is corresponding with each sample, chooses 0.7 < r < 0.99
Peak retain, it is achieved the elimination of false positive mass spectra peak；
Relative concentration index corresponding to QC_mix difference extension rate sample cycles of concentration sample different with QC_mix according to
The method comprised the steps as follows obtains: by QC_mix difference extension rate sample cycles of concentration sample different with QC_mix
It is X that the sample that in Ben, concentration is minimum gives relative concentration index, then by the QC_mix difference extension rate of remaining variable concentrations
Sample cycles of concentration sample different with QC_mix calculates according to the variable concentrations multiple of X and obtains, and obtains all QC_mix different
The relative concentration index of extension rate cycles of concentration different with QC_mix；
Formula 1: sample relative concentration this sample of index y=is relative to the concentration multiple * X of concentration smallest sample
X is the numerical value of 0；
(E) mass spectra peak processing obtain 0.7 < r < 0.9 through (D) is carried out the number of manual correction alignment integration mistake
Strong point, it is achieved eliminate false positive mass spectra peak.
Two, the method distinguishing different sample metabolite
Comprise the steps:
1, preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample, multiple sample to be tested metabolite sample；
QC_mix sample is to be mixed by the metabolite solution of multiple samples to be tested, obtains QC_mix sample；
Metabolite solution is to be made up of metabolite and organic solvent or with the metabolism of organic solvent extraction sample to be tested
Product obtains, as sample to be tested metabolite sample；
Solvent blank sample is made up of internal standard and organic solvent；
Different extension rate QC_mix samples for carrying out stepwise dilution, the different extension rates obtained to QC_mix sample
QC_mix sample；
Different cycles of concentration QC_mix samples for QC_mix sample is concentrated step by step, the different cycles of concentration obtained
QC_mix sample；
Multiple sample to be tested metabolite samples are the metabolite solution of multiple sample to be tested；
2, chromatograph-mass spectrometer coupling detecting step 1 successively) each sample of obtaining, detection comprises the steps:
(1) solvent blank sample is carried out more than or equal to 3 detections to completely overlapped (the i.e. instrument of total ions chromatogram
Device reaches steady statue), obtain the raw mass spectrum data more than or equal to 3 times of solvent blank sample；
(2) QC_mix sample is carried out more than or equal to 6 times detection, obtain QC_mix sample more than or equal to 6 times
Raw mass spectrum data；
(3) to different extension rate QC_mix samples and different cycles of concentration QC_mix samples according to concentration from little to
Big order detects 1 time successively, obtains different extension rate QC_mix samples that concentration arranges from small to large with the denseest
The raw mass spectrum data of demagnification number QC_mix sample；
(4) multiple sample to be tested metabolite samples are carried out respectively 1 detection, obtains multiple sample to be tested metabolism
The raw mass spectrum data of product sample；
3, all mass spectrometric datas obtaining step 2 carry out peak extraction and peak alignment, obtain the peak list square of all samples
Battle array；Filter the false positive peak in peak list matrix with the five step mass spectra peak filtering rules of following (A)-(E) again, obtain
Mass spectra peak after filtration:
(A) choose QC_mix sample in peak list matrix more than or equal to the peak frequency of occurrences in 6 testing results > be equal to
The peak of 80% retains；
(B) choose in (A) processes the peak that obtains QC_mix sample more than or equal to 6 testing results in peak variation
Property RSD < 20% peak retain；
(C) choose the B/S value peak less than 5% in (B) processes the peak obtained to retain；
B/S value is more than than QC_mix sample more than or equal to 3 testing result peak area meansigma methodss for solvent blank sample
In 6 testing result peak area meansigma methodss；
(D) QC_mix difference extension rate sample concentration times different with QC_mix in (C) processes the peak obtained is calculated
The pearson correlation coefficient r of the relative concentration index that the peak area of numerical example is corresponding with each sample, chooses 0.7 < r < 0.99
Peak retain, it is achieved the elimination of false positive mass spectra peak；
Relative concentration index corresponding to QC_mix difference extension rate sample cycles of concentration sample different with QC_mix according to
The method comprised the steps as follows obtains: by QC_mix difference extension rate sample cycles of concentration sample different with QC_mix
It is X that the sample that in Ben, concentration is minimum gives relative concentration index, calculates the relative of remaining concentration samples according to equation 1 below
CI y, obtains the relative concentration index of all QC_mix difference extension rate cycles of concentration different with QC_mix；
Formula 1: sample relative concentration this sample of index y=is relative to the concentration multiple * X of concentration smallest sample
X is the numerical value of 0；
(E) mass spectra peak processing obtain 0.7 < r < 0.99 through (D) is carried out the number of manual correction alignment integration mistake
Strong point, it is achieved eliminate false positive mass spectra peak.
4, after the filtration obtained by the E of step 3, the peak area of mass spectra peak and the relative concentration index of its correspondence are set up and are returned
Model；Again the mass spectrum peak area of the metabolite of each sample to be tested is substituted into regression model respectively be corrected and return
One changes, and obtains the relative concentration index of each sample to be tested；
Regression model include linear regression model (LRM), binomial regression model, logistic regression models, Exponential Regression Model and
/ or two-pass regression model；
5, the relative concentration index to all samples to be tested carries out multivariate or univariate statistics analysis, finds multiple treating
Test sample difference metabolite originally.
Above-mentioned chromatograph-mass spectrometer coupling is GC-MS, LC-MS and CE-MS；Chromatograph-mass spectrometer coupling is specially LC-MS；
Step 3) in, peak extract and peak alignment use software be mass spectral analysis software, specially xcms or
Masshunter Quanlitative Analysis；
Step 3) E in, the mass spectra peak processing obtain 0.9 < r < 0.99 through (D) is carried out manual correction alignment long-pending
Misclassification data point by mistake.
Embodiment 2, manual simulation sample verify that the inventive method reduces the effect of false positive mass spectra peak
For effectiveness, feasibility and the reliability of verification method, the experimental configuration artificial mould of 20 kinds of standard substance compositions
Intending sample (Artificial Samples, AS), Fig. 1 is the flow chart of the present invention.
Table 1 is manual simulation sample and the concentration (mmol) of 20 kinds of standard substance compositions
*: Asa:artificial samples group A, manual simulation sample A group, Asa_1, _ 2,, _ 6 simulations
6 secondary pollutants repeat.
^:Asb:artificial samples group B, manual simulation sample B group, Asb_1, _ 2,, _ 6 simulations 6
Secondary pollutant repeats.
Concentration unit is mmol
Standard substance: tyrosine (T2900000), phenylalanine (147966), nimbecetin (96353), 3-indole
Butanoic acid (57310), Taxifolin (78666), 5-hydroxyl color ammonia (14972), hair stamen flavone-7-O-β-D Fructus Vitis viniferae
Glucosides (1087483), 2-amino-4-fluobenzoic acid (370169), card grass amine (P562s), chlorogenic acid (C3878),
Scopolamine lactone (S2500), hydrocinnamoyl acid lactone (D104809), rosmarinic acid (36954), 6,7-dihydroxies
Butylcoumariii (246573), 3,4-dimethoxy-cinnamic acids (D133809), coumarin (72609)；Purchased from Sigma
Company (Sigma-Aldrich, Shanghai), is article No. in bracket.
Standard substance: tanshinone ⅡA (110766), cryptotanshinone (110852), Tanshinone I (110867) is purchased from
Biologics calibrating institute (Chinese), is article No. in bracket.
1, preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample
By 20 kinds of standard substance, according to the configuration concentration (mmol) in table 1, it is configured to 12 parts of manual simulation samples
Asa_1, Asa_2,, Asb6, solvent is methanol (chromatographic grade, Thermo fisher company), each manual simulation sample
The final constant volume of product, to 2mL, is inside designated as coumarin (being shown in Table 1).
Manual simulation sample A group, is Asa_1 including sample, Asa_2, Asa_3, Asa_4, Asa_5, Asa_6；
Manual simulation sample B group, is Asb_1 including sample, Asb_2, Asb_3, Asb_4, Asb_5, Asb_6；
Solvent blank sample is the hplc grade methanol containing internal standard (coumarin, 0.2mmol).
QC_mix sample for take manual simulation sample A group and B group totally 12 sample 50ul respectively, mixed in equal amounts, obtain
QC_mix；
Different extension rate QC_mix samples are by QC_mix hplc grade methanol stepwise dilution 2,4,8,16 times, obtain dilute
Release 2 times of QC_mix samples, 4 times of QC_mix samples of dilution, 8 times of QC_mix samples of dilution, 16 times of QC_mix samples of dilution；
Different cycles of concentration QC_mix samples for use frozen centrifugation (1500g) to concentrate QC_mix, concentration 2 times step by step,
4 times, obtain concentrating 2 times of QC_mix samples, concentrating 4 times of QC_mix samples (Fig. 3).
2, each sample is detected with liquid chromatography mass spectrometric combination
Chromatograph-mass spectrometer coupling detects following each sample successively: solvent blank pattern detection 3 times is complete to total ions chromatogram
Full weight folds (i.e. instrument reaches steady statue), QC_mix pattern detection 6 times, 16 times of QC_mix samples (DS_1/16x) of dilution
Detect 1 time, 8 times of QC_mix samples (DS_1/8x) of dilution detect 1 time, 4 times of QC_mix samples (DS_1/4x) of dilution
Detect 1 time, 2 times of QC_mix samples (DS_1/2x) of dilution detect 1 time, concentrate 2 times of QC_mix samples (DS_2x) inspections
Survey 1 time, concentrate 4 times of QC_mix samples (DS_4x) detect 1 time, 12 artificial analog samples detect 1 time, obtain various kinds
This mass spectrum initial data；
The condition of chromatograph-mass spectrometer coupling is as follows:
1) chromatographic condition: chromatographic apparatus, Agilent company 1290 type ultrahigh pressure liquid phase chromatograph (UHPLC), chromatographic column
ZORBAX Eclipse Plus C18 (Agilent), 2.1*100mm, packing material size 1.8 μm.
2) sample size 5ul, column temperature 30 degree, phase composition of flowing: A water (0.1%, formic acid), B acetonitrile (0.1%,
Formic acid), UHPLC gradient elution program is as follows: initial 5%B, 5min → 20%B, 15min → 40%B, 25min
→ 100%B, 28 → 100%B, 28.5 → 5%B balance 2.5min, whole elution time 30min.
3) Mass Spectrometry Conditions: chromatographic apparatus, Agilent company 6540 type quadrupole rod time-of-flight mass spectrometry (Q-TOF/MS),
It is equipped with high sensitivity and sprays sub-focusedion source (Jet stream ESI) of wandering about as a refugee.Full scan pattern (full scan),
Mass range 50-1000, acquisition rate, 2spectrum/s, correct ion: m/z 121.050873 under positive ion mode,
922.009798.Positive ion mode parameter is arranged: carrier gas temperature, 350 DEG C；Dry gas stream speed: 8L/min；Nebulizer
Pressure, 35psi；Sheath temperature, 350 DEG C；Sheath gas velocity, 8L/min；V cap voltage, 4000V；Fragmentor
Voltage, 130V；Nozzle voltage, 0V.Negative ion mode parameter is arranged: carrier gas temperature, 350 DEG C；It is dried gas
Flow velocity: 8L/min；Nebulizer pressure, 35psi；Sheath temperature, 350 DEG C；Sheath gas velocity, 8L/min；V cap
Voltage, 3000V；Fragmentor voltage, 130V；Nozzle voltage, 1000V.
3, mass spectrometric data is analyzed
1) mass spectrometric data pretreatment
To 3) the mass spectrum initial data of all samples that obtains carries out peak and extracts and peak aligns, and obtains the peak row of all samples
Table matrix；
Peak extract and peak alignment use software be mass spectrometric data pretreatment software, specifically include compound extract software:
MasshunterQuanlitative Analysis (B04) (Agilent) and peak alignment software: Mass Profiler
Professional (MPP, Agilent)；
Peak extracts and deconvolution, uses characterization of molecules to extract (Molecule FeatureExtraction, MFE) algorithm；
Key parameter is arranged: peak height >=500 countings, absolute peak height >=6000, metabolite extraction algorithm type, little molecularization
Compound (chromatograph), ionic species includes: cation, including+H ,+Na ,+K ,+NH4 etc., compound charge state,
1。
Peak alignment software Mass Profiler Professional (MPP, Agilent), all metabolic profiles spectrum is passed through
Above-mentioned MFE extracting method obtains the list at all detection peaks, and exports to CEF file, carries out peak pair in MPP software
Together, shape obtains the peak list matrix of all samples.
2) five step mass spectra peaks filter false positive peak
(A) repeatability inspection: choose the peak frequency of occurrences in 6 testing results of QC_mix sample in peak list matrix > etc.
Peak in 80% retains；Unstable peak for the random peak occurred, or may be measured in the peak filtered out.
(B) variability inspection: choose in (A) processes the peak that obtains peak in 6 testing results of QC_mix sample
< peak of 20% retains variability RSD；
(C) blank solvent inspection: choose the B/S value peak less than 5% in (B) processes the peak obtained and retain；
B/S value is that 3 testing result peak area meansigma methodss of solvent blank sample are than face, 6 testing result peaks of QC_mix sample
Long-pending meansigma methods；
B/S value is the lowest, illustrates that the amount that background contains is the fewest；B/S < 1% or 5%, less than general experimental error
In；B/S value is the highest, illustrates in solvent or instrumental background containing relevant ions, it may be possible to the mass signal of non-biological origin.
(D) quantitation capabilities inspection:
Calculate DS_1/8x in (C) processes the peak obtained, DS_1/4x, DS_1/2x, DS_1x, DS_2x, DS_4x
The peak area of sample and the pearson correlation coefficient r of relative concentration index, choose the peak reservation of 0.7 < r < 0.99, it is achieved
The elimination of false positive mass spectra peak；
Be 100 to DS_1/16x sample assignment relative concentration index X, then DS_1/8x, DS_1/4x, DS_1/2x, DS_1x,
Relative concentration index in DS_2x, DS_4x sample is respectively 200,400,800,1600,3200；
Relative concentration index (Relative Concentration Index, RCI), owing to not knowing each peak
Absolute concentration, but all of peak all has identical extension rate, it is assumed that all of peak has phase in DS sample
Same relative concentration index.
(E) manual examination (check): the mass spectra peak processing obtain 0.7 < r < 0.99 through (D) is carried out manual correction alignment long-pending
Misclassification data point by mistake, it is achieved eliminate false positive mass spectra peak.
The design sketch that five step peak filtering rules filter is shown in Fig. 4, and the false positive mass signal of 92.4% is filtered.
Fig. 2 is overlapping total ions chromatogram, and can distinguish which mass spectra peak in conjunction with diluted sample and solvent blank is
The peak of non-biological origin, which mass spectra peak has preferably or the quantitation capabilities of price differential.
Result is as follows:
1) all of 20 kinds of standard substance are all by five step peak filtering rules, altogether inside 1343 mass spectra peaks, protect
Stay 102, filter out the false positive mass spectra peak of 92.4%.
2) inside 102 retained mass spectra peaks, may determine that therein 53 by molecular weight and retention time
Individual derive from 20 kinds of standard substance and adduct ion, cracking ion；21 unknown compounds therein, have with standard substance
Having similar concentration ratio, they derive from standard substance, because standard substance purity may be able to not be wherein to 100%
Impurity component；Only 13 unknown peaks do not know that it is originated.
4, set up regression model and calculate the relative concentration index of 12 artificial analog samples
After the filtration obtain the E of step 3, the relative concentration index of the peak area of mass spectra peak and its correspondence is set up and is returned mould
Type；Again the mass spectrum peak area of the metabolite of each sample to be tested is substituted into regression model respectively to be corrected and normalizing
Change, obtain the relative concentration index of each sample to be tested.
Part regression model shown in Fig. 5, is several regression models set up according to relative concentration index and mass spectra peak area
And correction equation.In mass spectra peak (102 mass spectra peaks remained) after filtration (Fig. 4), the mass spectrum of 72.0%
Peak meets linearity correction model, and the peak of 21.8% meets binomial model, 3.4% meet logarithmic model, only one
Composite model is manually set up at peak, i.e. meets linear model in low concentration region, meets binomial model in high concentration region.
Again remain 102 regression models that mass spectra peak peak area is updated to each set up are corrected, obtain
Corresponding relative concentration index.
Embodiment 2, the method for the present invention are used for distinguishing the difference metabolite of different rice varieties
With typical indicia rice cropping kind 9311 and japonica rice cropping kind nipponbare LIPIDS OF DRY RICE EMBRYO seed
As a example by, compare the pluses and minuses of the present invention and tradition metabolism group method.
One, the present invention method and tradition metabolism group method
A, the present invention method as follows:
1, preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample, 2 rice varieties metabolite samples
Sample to be tested metabolite sample: each 10 parts of 9311 and nipponbare rice grains, every part of 100mg, use
Ball milling instrument (Retsch Mixer Mill MM 400, VerderRetsch Trading Co.Ltd., Shanghai, China)
Pulverizing, employing is designated as umbelliferone lactone in containing, and (concentration is for umbelliferon, Sigma company, article No. 93979
2mg/mL, 20 μ L) hplc grade methanol carry out ultrasonic (20kHz) extract 15min.Then it is centrifuged at 13,000rpm
10min, it is thus achieved that 9,311 10, Oryza sativa L. samples and the metabolism extract of 10 samples of nipponbare Oryza sativa L., be 10 9311
Oryza sativa L. metabolite sample and 10 nipponbare Oryza sativa L. metabolite samples.
Solvent blank sample is by internal standard umbelliferone lactone (umbelliferon, concentration 0.2mmol) and hplc grade methanol group
Become.
QC_mix sample: take 50ul volume respectively from 10 samples of 9311 Oryza sativa L. and 10 samples of nipponbare Oryza sativa L. and mix
Cooperation is QC_mix (all of biological specimen mixed in equal amounts)；
Different extension rate QC_mix samples: by QC_mix hplc grade methanol stepwise dilution 2,4,8 times, obtain diluting 2
Times QC_mix sample, 4 times of QC_mix samples of dilution, 8 times of QC_mix samples of dilution；
Different cycles of concentration QC_mix samples: QC_mix is used frozen centrifugation concentrate (1500g) concentrate step by step 2 times, 4
Times, obtain concentrating 2 times of QC_mix samples, concentrating 4 times of QC_mix samples.
2, each sample is detected with liquid chromatography mass spectrometric combination
Chromatograph-mass spectrometer coupling detects following each sample successively respectively: solvent blank sample (detects 3 times to total ion current color
Spectrogram is completely overlapped, i.e. instrument reaches steady statue), QC_mix sample (detecting 6 times), dilution 8 times of QC_mix samples
(DS_1/8x), 4 times of QC_mix samples (DS_1/4x) of dilution, 2 times of QC_mix samples (DS_1/2x) of dilution, concentration 2
Times QC_mix sample (DS_2x), concentrate 4 times of QC_mix samples (DS_4x), 10 9311 Oryza sativa L. metabolite samples and
10 nipponbare Oryza sativa L. metabolite samples, obtain the metabolism spectrum mass spectrometric data of each sample；
The condition of chromatograph-mass spectrometer coupling is with embodiment 1.
3, mass spectrometric data is analyzed
1) mass spectrometric data pretreatment: with embodiment 1；
2) five step mass spectra peaks filter: with embodiment 1；Obtain and eliminate false-positive mass spectra peak list.Result is shown in Fig. 6,71.4%
Mass spectra peak filtered.
4, set up regression model and calculate 9311 Oryza sativa L. metabolite samples and nipponbare Oryza sativa L. metabolite sample
Relative concentration index
After the filtration obtain the E of step 3, the relative concentration index of the peak area of mass spectra peak and its correspondence is set up and is returned mould
Type；Again by each 9311 Oryza sativa L. metabolite samples and the mass spectrum peak of each nipponbare Oryza sativa L. metabolite sample
Area substitutes into regression model respectively and is corrected and normalization, obtains each 9311 Oryza sativa L. metabolite sample relative concentrations
Index and each nipponbare Oryza sativa L. metabolite sample relative concentration index；
5, to all 9311 Oryza sativa L. metabolite sample relative concentration index and all nipponbare Oryza sativa L. metabolites
Sample relative concentration index carries out multivariate or univariate statistics analysis, find 9311 Oryza sativa L. metabolite samples and
The difference metabolite of nipponbare Oryza sativa L. metabolite sample.
The present invention carries out multivariate analysis based on RCI rather than peak area, and RCI has been normalized data, its numerical value
There is identical range scale, it is not necessary to mass spectra peak is normalized (normalization) and processes.
Directly set up principal component analysis (Principal component analysis, PCA) by RCI, partially minimum
Two take advantage of discriminant analysis (Partial least squares regressiondiscrimination analysis, PLS-DA)
Or orthogonal partial least-squares regressive analysis (orthogonal projections to latent structures, OPLS).
The standard of difference metabolite must is fulfilled for:
1) metabolite difference Fold Change>2 or<0.5 of content in two kinds of rice grains
2) p-value < 0.05 of t-test inspection
B, tradition metabolism group method
9311 Oryza sativa L. metabolite samples and 10 nipponbare Oryza sativa L. metabolites are surveyed in chromatography-mass spectroscopy joint inspection respectively, obtain
Obtain mass spectra peak, do not carry out filtering direct peak area and carry out normalization process, then carry out multivariate analysis (De
VosRCH etal.,Nat Protoc,2007,2:778-791)。
Two, the present invention method and tradition metabolism group methods and results comparison
The interpretability of the principal component model 1, distinguishing two kinds of rice cropping kinds is different with predictive ability
Multivariate statistical analysis result such as Fig. 7, the pca model using the present invention to obtain is obvious to the explanation percentage ratio of data
Higher than traditional method, and the predictive ability of model is also significantly greater than traditional method.
2, this method (right) is distinguishing rice grain principal component analysis shot chart ratio with tradition metabolism group method (left)
Relatively
Multivariate statistical analysis result as shown in Figure 8, indicia rice cropping kind 9311 and japonica rice cropping kind
Nipponbare rice grain metabolite difference is relatively big, and either traditional method or the method for the present invention, can distinguish
Both rice varieties.But two groups of experiment materials clearly distinguished by neoteric PCA collection of illustrative plates.
3, this method (under) with tradition metabolism group method (on) distinguish rice grain principal component analysis load diagram ratio
Relatively
Multivariate statistical analysis result, as it is shown in figure 9, the difference metabolite of S-plot display two groups of samples of metabolite, is distributed
Metabolite at S-plot two ends is the material having notable difference between two groups.This method substantially eliminates S-plot pars intermedia
The metabolite divided, eliminates false-positive peak significantly.
4, the ability of this method (circle on the left side) and tradition metabolism group (circle on the right) screening difference metabolite is compared
Multivariate statistical analysis result as shown in Figure 10, by identical screening criteria, Fold change>2 or<0.5,
P-value < 0.5 (t-test), traditional method obtains 575 difference metabolite, and new invention method obtains 135 differences
Different metabolite, two kinds of methods filter out total metabolite 121 kinds.The inventive method significantly reduces false-positive difference
Metabolite, is substantially reduced the scope of candidate's difference metabolite.
From the above, it can be seen that
(1) present invention is compared with traditional method, greatly eliminates false positive mass signal.
In the artificial sample of standard substance composition, eliminate the false positive mass signal of 92.4%, biological specimen is eliminated 71.4%
False positive mass signal.
(2) present invention introduces relative concentration index, set up regression correction model, improve quantitative analysis ability, to Oryza sativa L.
The principal component analysis of sample, detail improves solution to model and releases ability and predictive ability.
(3) present invention greatly reduces candidate's difference metabolite, provides basis for finding real biomarker.
(4) present invention can also pass through five step peak filtering rules, and non-targeted metabolism group method is converted into plan targeting metabolism
Group method, reduces the scope intending detection metabolite.
(5) relative isotope labelling of the present invention, for the targeted approach that standard substance are set up, the present invention is simple, low cost,
Efficiently.
Claims (7)
1. the method distinguishing the difference metabolite of multiple samples to be tested, comprises the steps:
1), preparation QC_mix sample, solvent blank sample, different extension rate QC_mix sample, different cycles of concentration
QC_mix sample, multiple sample to be tested metabolite sample；
Described QC_mix sample is to be mixed by the metabolite solution of multiple samples to be tested, obtains QC_mix sample；
Described metabolite solution is to be made up of metabolite and organic solvent or with organic solvent extraction sample to be tested
Metabolite obtains, as sample to be tested metabolite sample；
Described solvent blank sample is made up of internal standard and described organic solvent；
Described different extension rate QC_mix sample for carrying out stepwise dilution, the difference obtained to described QC_mix sample
Extension rate QC_mix sample；
Described different cycles of concentration QC_mix sample for described QC_mix sample is concentrated step by step, the difference obtained
Cycles of concentration QC_mix sample；
The plurality of sample to be tested metabolite sample is the metabolite solution of multiple sample to be tested；
2), chromatograph-mass spectrometer coupling detecting step 1 successively) each sample of obtaining, described detection comprises the steps:
(1) carry out described solvent blank sample, more than or equal to 3 detections, obtaining being more than of solvent blank sample
In the raw mass spectrum data of 3 times；
(2) described QC_mix sample is carried out more than or equal to 6 times detection, obtain QC_mix sample more than or equal to 6
Secondary raw mass spectrum data；
(3) to described different extension rate QC_mix samples and different cycles of concentration QC_mix samples according to concentration from
Little detect successively 1 time to big order, obtain different extension rate QC_mix samples that concentration arranges from small to large and not
Raw mass spectrum data with cycles of concentration QC_mix sample；
(4) the plurality of sample to be tested metabolite sample is carried out respectively 1 detection, obtains multiple sample to be tested
The raw mass spectrum data of metabolite sample；
3), to step 2) all mass spectrometric datas of obtaining carry out peak and extract and peak aligns, and obtain the peak row of all samples
Table matrix；The false positive in described peak list matrix is filtered again with the five step mass spectra peak filtering rules of following (A)-(E)
Peak, mass spectra peak after being filtered:
(A) the big more than or equal to the peak frequency of occurrences in 6 testing results of QC_mix sample in described peak list matrix is chosen
Retain in the peak equal to 80%；
(B) choose in (A) processes the peak that obtains QC_mix sample more than or equal to 6 testing results in peak variation
Property RSD less than 20% peak retain；
(C) choose the B/S value peak less than 5% in (B) processes the peak obtained to retain；
B/S value is more than than QC_mix sample more than or equal to 3 testing result peak area meansigma methodss for solvent blank sample
In 6 testing result peak area meansigma methodss；
(D) QC_mix difference extension rate sample concentration times different with QC_mix in (C) processes the peak obtained is calculated
The pearson correlation coefficient r of the relative concentration index that the peak area of numerical example is corresponding with each sample, chooses 0.7 < r < 0.99
Peak retain；
The relative concentration index that described QC_mix difference extension rate sample cycles of concentration sample different with QC_mix is corresponding
Obtain according to the method that comprises the steps as follows: by described QC_mix difference extension rate sample and described QC_mix not
Giving relative concentration index with the sample that concentration in cycles of concentration sample is minimum is X, calculates remaining according to equation 1 below dense
The relative concentration index y of degree sample, obtains the relative of all QC_mix difference extension rate cycles of concentration different with QC_mix
CI；
Formula 1: sample relative concentration this sample of index y=is relative to the concentration multiple * X of concentration smallest sample
X is the numerical value of 0；
(E) mass spectra peak processing obtain 0.7 < r < 0.99 through (D) is carried out the number of manual correction alignment integration mistake
Strong point, it is achieved eliminate false positive mass spectra peak；
4), by step 3) the described filtration that obtains of E after the peak area of mass spectra peak and the relative concentration index of its correspondence build
Vertical regression model；Again the mass spectrum peak area of the metabolite of each described sample to be tested is substituted into regression model respectively to enter
Row correction and normalization, obtain the relative concentration index of each sample to be tested；
Described regression model includes linear regression model (LRM), binomial regression model, logistic regression models, index return mould
Type and/or two-pass regression model；
5), the relative concentration index of all samples to be tested is carried out multivariate or univariate statistics analysis, find multiple treating
Test sample difference metabolite originally.
Method the most according to claim 1, it is characterised in that:
Step 2) in, described chromatograph-mass spectrometer coupling is GC-MS, LC-MS and CE-MS；
And/or, described chromatograph-mass spectrometer coupling is specially LC-MS；
Step 3) in, the software that described peak extracts and peak alignment uses is mass spectrum Preprocessing software,
And/or, described mass spectrum Preprocessing software is specially xcms or MasshunterQuanlitative
Analysis；
Step 3) E in, the mass spectra peak processing obtain 0.9 < r < 0.99 through (D) is carried out manual correction alignment long-pending
Misclassification data point by mistake.
Method the most according to claim 1 and 2, it is characterised in that:
Step 1) in, described sample to be tested is biogenetic derivation sample and/or non-biological origin sample；
Described biogenetic derivation sample is specially plant, animal and/or microorganism.
4. according to described method arbitrary in claim 1-3, it is characterised in that:
Step 1) in, the plurality of sample to be tested number is more than or equal to 2；
Step 2) in, described QC_mix sample is to be uniformly mixed so as to obtain by the metabolite solution equal-volume of multiple samples to be tested；
The diluent that described dilution uses is described organic solvent；
Described stepwise dilution is that stepwise dilution is to 16-32 times；
Described concentration step by step is for be concentrated into 2-4 times step by step.
5. a false positive mass spectra peak removing method in metabolite detection, comprises the steps: as claim 1-4
Step 1 in middle either method)-3), it is achieved false positive mass spectra peak eliminates.
6. in claim 1-3 arbitrary described method or the method described in claim 5 at quantitative verification difference sample
Metabolite in application.
7. the application in false positive mass spectra peak signal in distinguishing sample of the method described in claim 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610346054.2A CN106018600B (en) | 2016-05-23 | 2016-05-23 | A kind of metabolism group method for distinguishing false positive mass spectrum peak-to-peak signal and quantitative correction mass spectrum peak area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610346054.2A CN106018600B (en) | 2016-05-23 | 2016-05-23 | A kind of metabolism group method for distinguishing false positive mass spectrum peak-to-peak signal and quantitative correction mass spectrum peak area |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106018600A true CN106018600A (en) | 2016-10-12 |
| CN106018600B CN106018600B (en) | 2018-06-01 |
Family
ID=57096728
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610346054.2A Active CN106018600B (en) | 2016-05-23 | 2016-05-23 | A kind of metabolism group method for distinguishing false positive mass spectrum peak-to-peak signal and quantitative correction mass spectrum peak area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106018600B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856310A (en) * | 2018-12-17 | 2019-06-07 | 大连理工大学 | The method of false positive mass spectral characteristic in removal metabolin quasi-molecular ions table based on HPLC-MS |
| CN110392829A (en) * | 2017-03-16 | 2019-10-29 | 百特基公司 | Quality guiding separation |
| CN111370067A (en) * | 2020-02-28 | 2020-07-03 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | LC/GC-MS-oriented metabonomics data quality control method and system |
| CN111426778A (en) * | 2020-04-30 | 2020-07-17 | 上海海关动植物与食品检验检疫技术中心 | Method for rapidly identifying olive oil grade based on high-resolution mass spectrometry technology combined with pattern recognition analysis |
| CN111650271A (en) * | 2020-06-23 | 2020-09-11 | 南京财经大学 | Identification method and application of soil organic matter marker |
| CN113295759A (en) * | 2021-05-31 | 2021-08-24 | 西南大学 | Method for screening differential metabolites in metabolome |
| WO2021217745A1 (en) * | 2020-04-26 | 2021-11-04 | 南京大学 | High-throughput screening method for non-target biomarker employing pollutant metabolic turbulence |
| US11226318B2 (en) | 2020-04-26 | 2022-01-18 | Nanjing University | Method for high-throughput screening of non-target biomarkers based on metabolic perturbation caused by pollutants |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003058238A1 (en) * | 2002-01-10 | 2003-07-17 | Max-Planck-Gesellschaft Zur Förderung Der Wissenschaften | Combined metabolomic, proteomic and transcrptomic analysis from one, single sample and suitable statistical evaluation of data |
| US20060200316A1 (en) * | 2005-03-01 | 2006-09-07 | Harin Kanani | Data correction, normalization and validation for quantitative high-throughput metabolomic profiling |
| CN102788849A (en) * | 2012-06-01 | 2012-11-21 | 中国人民解放军第二军医大学 | Novel method for chromatography-mass spectrometry metabolomics data analysis |
| CN105334279A (en) * | 2014-08-14 | 2016-02-17 | 大连达硕信息技术有限公司 | High-resolution mass spectrum data processing method |
| CN105424827A (en) * | 2015-11-07 | 2016-03-23 | 大连理工大学 | Screening and calibrating method of metabolomic data random errors |
-
2016
- 2016-05-23 CN CN201610346054.2A patent/CN106018600B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003058238A1 (en) * | 2002-01-10 | 2003-07-17 | Max-Planck-Gesellschaft Zur Förderung Der Wissenschaften | Combined metabolomic, proteomic and transcrptomic analysis from one, single sample and suitable statistical evaluation of data |
| US20060200316A1 (en) * | 2005-03-01 | 2006-09-07 | Harin Kanani | Data correction, normalization and validation for quantitative high-throughput metabolomic profiling |
| CN102788849A (en) * | 2012-06-01 | 2012-11-21 | 中国人民解放军第二军医大学 | Novel method for chromatography-mass spectrometry metabolomics data analysis |
| CN105334279A (en) * | 2014-08-14 | 2016-02-17 | 大连达硕信息技术有限公司 | High-resolution mass spectrum data processing method |
| CN105424827A (en) * | 2015-11-07 | 2016-03-23 | 大连理工大学 | Screening and calibrating method of metabolomic data random errors |
Non-Patent Citations (4)
| Title |
|---|
| ELIZABETH J WANT等: "Global metabolic profiling procedures for urine using UPLC–MS", 《NATURE PROTOCOLS》 * |
| EVA ZELENA等: "Development of a Robust and Repeatable UPLC-MS Method for the Long-Term Metabolomic Study of Human Serum", 《ANAL. CHEM.》 * |
| 孙海涛等: "基于LC／MS的代谢组学数据并行处理研究", 《质谱学报》 * |
| 林晓惠等: "MS-IAS：集成的质谱代谢组学数据分析系统", 《分析化学研究学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110392829A (en) * | 2017-03-16 | 2019-10-29 | 百特基公司 | Quality guiding separation |
| CN109856310A (en) * | 2018-12-17 | 2019-06-07 | 大连理工大学 | The method of false positive mass spectral characteristic in removal metabolin quasi-molecular ions table based on HPLC-MS |
| CN109856310B (en) * | 2018-12-17 | 2020-06-16 | 大连理工大学 | Method for removing false positive mass spectrum characteristics in metabolite ion peak table based on HPLC-MS |
| CN111370067A (en) * | 2020-02-28 | 2020-07-03 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | LC/GC-MS-oriented metabonomics data quality control method and system |
| CN111370067B (en) * | 2020-02-28 | 2023-05-23 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | LC/GC-MS-oriented metabonomics data quality control method and system |
| WO2021217745A1 (en) * | 2020-04-26 | 2021-11-04 | 南京大学 | High-throughput screening method for non-target biomarker employing pollutant metabolic turbulence |
| US11226318B2 (en) | 2020-04-26 | 2022-01-18 | Nanjing University | Method for high-throughput screening of non-target biomarkers based on metabolic perturbation caused by pollutants |
| CN111426778A (en) * | 2020-04-30 | 2020-07-17 | 上海海关动植物与食品检验检疫技术中心 | Method for rapidly identifying olive oil grade based on high-resolution mass spectrometry technology combined with pattern recognition analysis |
| CN111650271A (en) * | 2020-06-23 | 2020-09-11 | 南京财经大学 | Identification method and application of soil organic matter marker |
| CN111650271B (en) * | 2020-06-23 | 2022-12-13 | 南京财经大学 | Identification method and application of soil organic matter marker |
| CN113295759A (en) * | 2021-05-31 | 2021-08-24 | 西南大学 | Method for screening differential metabolites in metabolome |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106018600B (en) | 2018-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106018600B (en) | A kind of metabolism group method for distinguishing false positive mass spectrum peak-to-peak signal and quantitative correction mass spectrum peak area | |
| CN104297355B (en) | Simulative-target metabonomics analytic method based on combination of liquid chromatography and mass spectrum | |
| Schripsema | Application of NMR in plant metabolomics: techniques, problems and prospects | |
| CN111579665B (en) | UPLC/HRMS-based metabonomics relative quantitative analysis method | |
| CN106770862A (en) | A kind of Classification of Tea method | |
| CN105675788B (en) | The method of progesterone and testosterone in high performance liquid chromatography tandem mass spectrum technology for detection saliva | |
| CN109696510A (en) | The method for obtaining transgenosis and non-transgenic corn Difference of Metabolism based on UHPLC-MS | |
| CN109187614A (en) | Based on nuclear magnetic resonance and mass spectrographic metabolism group data fusion method and its application | |
| CN109870536A (en) | A kind of high covering iipidomics analysis method based on liquid chromatograph mass spectrography | |
| Mason et al. | Miniaturized 1 H-NMR method for analyzing limited-quantity samples applied to a mouse model of Leigh disease | |
| CN107085061A (en) | The absolute quantification analysis method of lysophosphatidyl choline based on HPLC MS/MS detection platforms | |
| KR20190042476A (en) | Biomarker for the discrimination of geographical origins of the soybeans and method for discriminating of geographical origin using the same | |
| CN105891364A (en) | Method and kit for detecting melatonin in saliva with HPLC-MS/MS (high performance liquid chromatography-tandem mass spectrometry) technology | |
| Cui et al. | Source identification of heroin by rapid detection of organic impurities using direct analysis in real time with high-resolution mass spectrometry and multivariate statistical analysis | |
| CN112435719A (en) | Method for rapidly searching liquid chromatography separation conditions of compound traditional Chinese medicine analysis based on chromatography retention multivariate statistical model | |
| CN107941939A (en) | A kind of method that organic rice and non-organic rice are distinguished using metabonomic technology | |
| US20110089318A1 (en) | Apparatus system and method for mass analysis of a sample | |
| Deng et al. | Quality assessment and origin tracing of Guangdong Liangcha granules using direct mass spectrometry fingerprinting | |
| CN102741689B (en) | Method for discovering pharmacologically active substance of natural products using high resolution mass spectrometry and pharmacological activity test | |
| CN108287200A (en) | Materials analysis methods of the mass spectrum with reference to the method for building up of database and based on it | |
| CN111289678A (en) | Rhizoma anemarrhenae quality detection method based on UPLC-QQQ-MS/MS method | |
| CN105938102A (en) | Method for rapid determination of pesticide residues in fruits and vegetables through chemical developing method | |
| Simmler et al. | Botanical integrity: Part 2: traditional and modern analytical approaches | |
| CN111239237B (en) | High-throughput mass spectrometry detection kit and quality detection method thereof | |
| WO2021142783A1 (en) | High-throughput mass spectrometry detection kit and quality inspection method therefor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |