CN102323365B - Construction method of mass spectrum model for detecting type I diabetes characteristic protein - Google Patents
Construction method of mass spectrum model for detecting type I diabetes characteristic protein Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention provides a mass spectrum model for detecting type I diabetes characteristic protein, which comprises the following mass-to-charge ratios: 7010.32, 8141.47, 7597.7, 7645.62, 3314.94, 8862.88, 7765.57, 9062.14, 7921.58, 7832.64, 6630.96, 4643.76, 9289.11, 8565.22, 3302.42, 9429.72, 1984.14 m/z, wherein when peaks of 3314.94, 6630.96, 3302.42, and 1984.14 m/z have up-regulated expression, and peaks of the remaining mass-to-charge ratios have down-regulated expression, a patient with type I diabetes or a potential patient is indicated. The mass spectrum model of the invention is applicable to the early detection and screening of type I diabetes; the method is simple and easy to operate, has high accuracy; and the invention provides a new thought for the early detection and screening of type I diabetes.
Description
Technical field
The present invention relates to the diabetes detection field, be a kind of brand-new noninvasive detection method, external to diabetes, particularly discovery and the detection carried out of type i diabetes, it is above that its susceptibility and specificity all reach.
Background technology
Diabetes are the metabolic diseases that cause due to insufficient insulin.Essential characteristic is long-term hyperglycaemia.Insulin is β emiocytosis in pancreas, is indispensable a kind of hormone in glucose metabolism.Work as hypoinsulinism, glucose utilization and storage are obstructed, and the concentration of glucose in blood (blood sugar concentration) will raise.Blood sugar concentration surpasses certain level, and unnecessary glucose just excretes by kidney, at this moment casts one's water and can find the glucose in urine positive, therefore claim diabetes.Diabetes are divided primary and the large class of Secondary cases two.Primary diabetes mellitus refers to that its pathogenic factor is not yet clear and definite, also claims idiopathic diabetes or agnogenio property diabetes, accounts for the overwhelming majority.General alleged diabetes namely refer to primary diabetes mellitus.Can divide following a few type:
1 insulin-dependent diabetes mellitus (IDDM, I type)
This type accounts for the 5%-10% of diabetes sum, and age of onset is less than 40 years old more, is mainly childhood and teenager, occasionally in the adult of non-obesity.Insulin is few or lack in patient body, and during morbidity, diabetic symptom is more obvious, and the state of an illness is heavier, has Ketosis-prone (referring to easy occurrence of diabetes ketoacidosis) must rely on exogenous insulin and makes a living, in case termination insulinize can life-threatening.
2 Non-Insulin Dependent Diabetes Mellituss (NIDDM, II type)
This type account for the diabetes sum 90% or more than, more than 40 years old sequela, onset is slow, concealment, atypical symptoms, the state of an illness is lighter, some patient finds diabetes because artery sclerosis, coronary heart disease, kidney trouble or ophthalmology disease are medical, and some is when health examination, just to find to suffer from diabetes.These type diabetes are generally without Ketosis-prone, keep on a diet or add with the oral antidiabetic drug treatment and just can control the state of an illness, a few patients is the necessary insulinize of control blood sugar, but does not rely on exogenous insulin (insulin of namely stopping using is unlikely and threatens patients ' lives).
The morbidity rate of China's diabetes and IGT is respectively 2.51% and 3.27% at present, diabetes prevalence superlatively district is Xinjiang autonomous region Karamay City (4.6%), next is Beijing area (3.99%), and Zhejiang Province's diabetes prevalence is 2.96%.Although it is still lower that the diabetes mellitus in China morbidity rate is compared with World Developed Countries, Chinese population is numerous, and the diabetes patient who estimates existing 25-64 year approximately 15,000,000.Another observes discovery, the incidence of disease that is greater than adult's diabetes of 25 years old surpasses 1,30/,100,000, reckoning is in being greater than the population of 25 years old, 700,000 diabetes new patients will be arranged every year, investigation is also found, diabetes prevalence is along with age, body weight are also found, diabetes prevalence increases along with the increase of age, body weight and average income, and overweight people's morbidity rate is about 5 times of regular severe one.Urine is sick to patients ' life quality and healthy having a strong impact on, and the cost that the control diabetes are paid is embodied in the following aspects: mortality ratio increases 2-3 doubly; Suffer from a heart complaint and the apoplexy probability increases 2-3 doubly; Many 10 times than the normal person of blind persons; Gangrene and amputee than the normal person many 20 times.Cause the main cause of fatal kidney trouble.Both at home and abroad also not based on the diabetes diagnosis method of serum polypeptide, we provide a kind of method that can be used for diagnosing based on mass spectral:mass spectrographic proteomic techniques at application now.
Ground substance assistant laser desorption ionization flight time mass spectrum (MALDI-TOF-MS) is a kind of novel soft ionization biological mass spectrometry that development in recent years is got up, its principle is with Ear Mucosa Treated by He Ne Laser Irradiation sample and substrate formed cocrystallization film, matrix passes to biomolecule from laser, absorbing energy, and in ionization process, proton translocation is obtained to proton to biomolecule or from biomolecule, and make the process of biomolecule ionization.The principle of TOF is that ion accelerates to fly over dirft tube under electric field action, according to the flight time difference that arrives detecting device, is detected the mass-to-charge ratio (M/Z) of namely measuring ion and is directly proportional to the flight time of ion, detects ion.Although the accuracy of MALDI-TOF is up to 0.1%～0.01%, far away higher than current conventional SDS electrophoresis and the efficient gel chromatographic technique of applying, but in the application of diabetes mark, still have some defects, therefore domestic there is no up to now adopts the MALDI-TOF-MS technology to obtain the report that detects the diabetes serum characteristic protein.
Summary of the invention
In order to address the above problem, the object of the invention is to provide a kind of mass spectra model for detection of the type i diabetes characteristic protein and preparation method thereof.
To achieve these goals, at first the present invention provides a kind of mass spectrum mark for detection of I type type i diabetes characteristic protein, and it comprises that mass-to-charge ratio is: 7010.32,8141.47,7597.7,7645.62,3314.94,8862.88,7765.57,9062.14,7921.58,7832.64,6630.96,4643.76,9289.11,8565.22,3302.42,9429.72 and the peak of 1984.14m/z.
The present invention also provides a kind of mass spectra model for detection of the type i diabetes characteristic protein, in this mass spectra model, comprising mass-to-charge ratio is 7010.32m/z, 8141.47m/z, 7597.7m/z, 7645.62m/z, 3314.94m/z, 8862.88m/z, 7765.57m/z, 9062.14m/z, 7921.58m/z, 7832.64m/z, 6630.96m/z, 4643.76m/z, 9289.11m/z, 8565.22m/z, 3302.42m/z, 9429.72m/z and the peak of 1984.14m/z, wherein, work as 3314.94m/z, 6630.96m/z, 3302.42m/z and the peak up-regulated expression of 1984.14m/z, the peak of all the other mass-to-charge ratioes is all lowered while expressing, be shown in advance type i diabetes patient or potential patient.The critical value of the up-regulated expression of described 3314.94m/z, 6630.96m/z, 3302.42m/z and 1984.14m/z characteristic protein is respectively 31.44 ± 8.94,85.84 ± 51.23,9.70 ± 3.31 and 20.01 ± 5.07.Described 7010.32m/z, 8141.47m/z, 7597.7m/z, 7645.62m/z, 8862.88m/z, 7765.57m/z, 9062.14m/z, 7921.58m/z, 7832.64m/z, 4643.76m/z, 9289.11m/z, 8565.22m/z and the characteristic protein of 9429.72m/z to lower that the critical value of expressing divides be separately 34.36 ± 9.05, 15.17 ± 4.61, 9.45 ± 2.58, 14.69 ± 3.31, 17.10 ± 5.98, 115.29 ± 47.08, 24.36 ± 9.51, 16.19 ± 4.34, 11.59 ± 3.81, 202.98 ± 98.84, 960.32 ± 365.62, 8.40 ± 1.64 and 45.82 ± 19.61.
And then, the invention provides a kind of method of setting up described mass spectra model, comprise the steps:
1) serum of collecting the type i diabetes patients serum of many cases clinical definite and normal control personnel is as two groups of serum specimens, carries out cryogenic freezing standby;
2) haemocyanin is carried out to pre-service before mass spectrum;
3) two groups of pretreated haemocyanins are carried out to Mass Spectrometer Method and read, obtain the finger-print of two groups of serum polypeptides;
4) standardization is carried out in the finger-print of all diabetics and normal human serum polypeptide, and collect data;
5) the data obtained is carried out to standardization, 7010.32,8141.47,7597.7,7645.62,3314.94,8862.88,7765.57,9062.14,7921.58,7832.64,6630.96,4643.76,9289.11,8565.22,3302.42,9429.72,1984.14m/z filter out the diabetic character albumen with following mass-to-charge ratio peak:, set up according to these 17 mass-to-charge ratio peaks the mass spectra model that detects the diabetic character albumen.
Wherein, described step 2) pretreated method is haemocyanin or the polypeptide adopted in magnetic beads for purifying and stable sample.
Wherein, described step 3) adopt the WCX2 chip to adsorb two groups of haemocyanins, and two groups of haemocyanins that are combined on weak cation WCX2 chip are read, obtain the finger-print of two groups of serum polypeptides.
The detection method of the present invention and other diabetes relatively, has the following advantages:
First, the present invention adopts diabetic and the discrepant a plurality of characteristic protein combinations of normal person's tool to carry out the detection to diabetes serum, and the method that has adopted traditional statistics to combine with modern bioinformatics method is carried out the data processing, thereby obtain diabetic and Healthy Human Serum protein fingerprint pattern detection model, and a series of protein mass-to-charge ratioes peak of finding provides the foundation and resource for finding new more preferably disease marker.
The second, with Serology test in the past, relatively have higher susceptibility and specificity, and can be for screening antidiabetic medicine.
The 3rd, the construction method of model of the present invention is reasonable in design feasible, for the clinical cure rate that diabetes are provided provides new screening method, also for the mechanism of exploring tumor development, provides new thinking simultaneously.
The 4th, utilize the present invention to analyze 50 parts of blood serum samples, the result shows, 50 routine correct judgments, recall rate reaches 100%, and specificity is 100%, and sensitivity is 100%, so the present invention can make diagnosis to diabetes, raising patient's survival rate and quality of life.
The accompanying drawing explanation
Fig. 1 is part Healthy Human Serum and type i diabetes patients serum's polypeptide collection of illustrative plates.
Fig. 2 repeats to do 5 Sigma serum mass spectrum fingerprint images of sample.
Fig. 3 is that the expression of protein peak in all established model samples showed, the arrow points specific charge is the type i diabetes characteristic protein peak of 7010.32m/z; The characteristic protein band of the 7010.32m/z of arrow points established model wherein, redness represents normal group, green represents the type i diabetes group.
Fig. 4 is that the expression of protein peak in all established model samples showed, the arrow points specific charge is the type i diabetes characteristic protein peak of 3314.94m/z; The characteristic protein band of the 3314.94m/z of arrow points established model wherein, redness represents normal group, green represents the type i diabetes group.
Fig. 5 is that the expression of protein peak in all established model samples showed, the arrow points specific charge is the type i diabetes characteristic protein peak of 7765.57m/z; The characteristic protein band of the 7765.57m/z of arrow points established model wherein, redness represents normal group, green represents the type i diabetes group.
Embodiment
Following examples are used for the present invention is described, but are not used for limiting the scope of the invention.
1, sample and instrument:
Totally 50 routine serum samples, wherein 25 examples are from the type i diabetes patient, and other 25 examples are from healthy population.All pathological replacement is definite after diagnosing for all 25 routine type i diabetes patients.All serum samples are lower extraction on an empty stomach in the morning all, after separation of serum, is stored in-80 low temperature refrigerators.
Ground substance assistant laser is resolved the WCX magnetic bead kit of flight time mass spectrum AutoflexII TOF/TOF and experiment use and is developed by U.S. Bruker company.The data analysis software Clinprotools of use Bruker company does the pre-service of data. the liquid phase systems Nano Aquity UPLC of application Waters company and the mass spectrometer system LTQ ObitrapXL (Thermo) of ThermoFisher company, Venus magnesphere Identification of Fusion Protein.
2, the sampling and processing of serum
Collect venous blood in the BD pipe, avoid haemolysis.Upper and lower oscillating tube is five times lentamente, and the coagula in blood is mixed.Room temperature (25 ℃) blood coagulation 1 hour, the vertical placement.Wherein blood must accurately condense one hour, otherwise, because sample causes different peptides to be composed different setting times.Under room temperature, with the centrifugal SST of 1.400-2.000g, managed (vacuum test tube, BD company) ten minutes with clinical centrifuge.Draw serum (supernatant) in the pipe of mark of correspondence.The 0.5ml centrifuge tube that mark is clean, same blood serum sample 50 μ l mono-pipes, packing multitube.Frozen blood serum sample is in-80 ℃ immediately.Because the multigelation blood serum sample easily causes the polypeptide precipitation, thereby make peptide spectrum lost part polypeptide, should avoid multigelation.Frozen serum is divided into persistence and to be packed.Can be-80 ℃ of preservations for many years after the serum packing.
The magnetic bead of blood serum sample is processed: before carrying out the ClinProt experiment, from low temperature refrigerator, extract each 1 pipe of blood serum sample of packing, be put in and wet on ice.Thawed 60～90 minutes.Take out 10 μ l magnetic bead binding buffer liquid (BS), the bead suspension that 10 μ l mix, 5 μ l blood serum samples, to sample hose, mix.After the standing 5min of room temperature, sample hose is put into to the magnetic bead separation vessel.Made magnetic bead adherent 1 minute, the fluid separation applications of magnetic bead and suspension, suck the liquid of suspension, then add 100 μ l magnetic bead cleaning buffer solutions (WS) in sample hose, mobile example pipe 10 times repeatedly between adjacent two holes before and after the magnetic bead separation vessel.Make for the last time sample hose standing on the magnetic bead separation vessel, the fluid separation applications of magnetic bead and suspension, suck the liquid of suspension.Repeat from adding 100 μ l magnetic bead cleaning buffer solutions, to the operation steps that finally sucks suspension liquid totally 3 times.From the magnetic bead separation vessel, take off sample hose, and add 5 μ l magnetic bead elution buffers (ES) in sample hose, dissolve adherent magnetic bead, sample hose is put into the magnetic bead separation vessel, the adherent 2min of magnetic bead, magnetic bead is with after the liquid of suspension fully separates, and supernatant is moved into to clean 5 μ l magnetic bead stabilizing buffer (SS) the 0.5ml sample hoses that just add.
3. bioinformatics method
(1) mass spectrometric data collection
Application AutoflexIITOF/TOF mass spectrometer.During laser energy 50%, the 10shots impurity elimination, in the time of 36%, 50shots gathers some points of a sample crystallization point, and on average each sample crystallization point is collected 400shots altogether 8 times.Laser frequency: 50Hz.Data Collection scope: 1-20KDa.Before every 8 sample crystallization points are collected data, carry out the external standard correction with standard items, the mean molecular weight deviation is less than 100ppm.Referring to Fig. 1, in Fig. 1, be the serum polypeptide fingerprint chromatogram, first three (sigmaA-C) is Healthy People, latter three is the type i diabetes patient's.
The experiment Quality Control: (1) for each original collection of illustrative plates collected, we set the peak number amount of S/N>=5 as a standard passing judgment on graph-spectrum quality; For the peak number amount, be greater than 50 collection of illustrative plates and just preserve, give up the collection of illustrative plates that the peak number amount is less than 50.(2) for whole experimental implementation, adopt the group within variance coefficient of Sigma serum to guarantee the consistance of testing, the coefficient of variation of this case method is 16.2%, meets the consistance allowed band, the illustrative experiment consistance is good, referring to table 1, Fig. 2.Table 1 is the value for coefficient of variation of 12 protein peaks in Sigma serum; Fig. 2 is the finger-print of 5 Sigma A-E serum in experiment.
The group within variance coefficient of table 1Sigma serum
(2) raw data pre-service
Raw data is processed through Bruker company data analysis software Clinprotools, and the peak value of 800-10K is done baseline calibration via Top hat method, and minimum baseline width 10%, with 10% minimum threshold values cluster; Then by the total ion current method, do normalized.
(3) selection of type i diabetes characteristic protein
Each mass-to-charge ratio protein peak is different to the relative importance of the differentiation of Different categories of samples, has used wilcoxon check P value to estimate the relative importance of each protein peak, and to respective egg evaluation in vain.
(4) neural network algorithm
The analysis of neural network method is from Neuropsychology and cognitive science achievement in research, a kind of disposal route with highly-parallel computing power, self-learning ability and fault-tolerant ability that application of mathematical method grows up.
Nerual network technique has showed at aspects such as pattern recognition and classification, identification filtering, control automatically, predictions the superiority that it is outstanding.Neural network is from Neuropsychology and cognitive science achievement in research, and a kind of parallel distributed mode treatment system that application of mathematical method grows up, have highly-parallel computing power, self-learning ability and fault-tolerant ability.The structure of neural network is comprised of an input layer, several middle hidden layers and an output layer.The analysis of neural network method, can be from finding its rule by unceasing study a large amount of complex data of unknown pattern.Neural net method has overcome the complicacy of traditional analysis process and has selected the difficulty of suitable pattern function form, and it is a kind of natural Nonlinear Modeling process, does not need to distinguish to have which kind of nonlinear relationship, brings great convenience for modeling and analysis.In example, neural network model is optimized the classification capacity of itself in conjunction with Batch-Neural-Gas algorithm.
In neural network training Algorithm for Training process, introduce the process of cross validation, adopted 80% in random selection sample to set up model here, 20% remaining conduct checking.It can the supervised training process, avoids the model of setting up to occur modeling sample is done very well, to the forecast sample performance poor " cross study " phenomenon.
(5) Identification of Fusion Protein
Magnesphere
After determining peak to be identified according to one's analysis, counter looking into processed the highest sample of peak strength to be identified in sample early stage.
Find out previous experiments magnetic bead used.20 parts of this sample parallel processing.Enrichment is a pipe.
Centrifugal 1300rmp 5min.On magnet stand, get supernatant.Avoid leaving magnetic bead and affect later experiments.
Liquid is spin-dried for to mark.
Adopt the Nano Aquity UPLC of Waters company liquid phase systems: the following setting of parameter, trapping column:
C18,5 μ m, 180 μ m * 20mm, nanoAcquity
TMColumn; Analytical column:
C18,1.7 μ m, 75 μ m * 150mm, nanoAcquity
TMColumn; Mobile phase A: 5% acetonitrile, the aqueous solution of 0.1% formic acid; Mobile phase B: 95% acetonitrile, the aqueous solution of 0.1% formic acid, all solution is the HPLC level.Trapping flow velocity 15 μ l/min, trapping time 3min, analyze flow velocity 300nl/min; Analysis time 60min, 35 ℃ of chromatogram column temperatures; Partial Loop pattern sample introduction, sampling volume 18 μ l.
The gradient elution programming:
Adopt the LTQ Obitrap XL of ThermoFisher company (Thermo) mass spectrometer system, Nano electric spray ion source (Michrom), spray voltage 1.4kV; Scanning of the mass spectrum time 60min; Experiment model is data dependence (Data Dependent) and dynamically eliminating (Dynamic Exclusion), and each parent ion carries out getting rid of 60 seconds after 2 MS/MS; Sweep limit 400-2000m/z; Obitrap is used in one-level scanning (MS), and resolution setting is 60000 (m/z 400 places); LTQ is used in CID and secondary scanning; The single isotope of choosing 10 ions that intensity is the strongest in the MS spectrogram carries out MS/MS (single electric charge is got rid of, not as parent ion) as parent ion.Usage data analysis software BioworksBrowser 3.3.1SP1 carries out Sequest
TMRetrieval, searching database is IPI Human (version 3 .45,71983 entries), for reducing false positive in additional its anti-storehouse of database end.The parent ion error is set as 50ppm, and the fragmention error is made as 1Da, and enzyme butt formula is that non-enzyme is cut.The result for retrieval setting parameter is deltacn>=0.10, two electric charge Xcorr2.0, tricharged Xcorr 2.5, the above Xcorr3.0 of tricharged, peptideprobability<=1e-003.The peptide section and the albumen result precision that under this Parameter Conditions, show are higher, stipulate to set according to document and international protein group.
Embodiment 2
The detection method of the mass spectra model of Application Example 1 and embodiment 1, to healthy population 25 examples, the serum proteins collection of illustrative plates of type i diabetes patient 25 examples has been done to detect and has been analyzed.With the mass-to-charge ratio peak of preliminary screening, be less than 0.05 according to the P value and be the significant differential protein of statistical discrepancy peak.
Healthy population 25 examples, the routine sample of type i diabetes patient 25 is given birth to the peak value of 84 molecular weight through the method common property in embodiment 1, further by neural network, filtering out specific charge is 17 protein peaks of 7010.32,8141.47,7597.7,7645.62,3314.94,8862.88,7765.57,9062.14,7921.58,7832.64,6630.96,4643.76,9289.11,8565.22,3302.42,9429.72,1984.14, can reach accuracy rate preferably, referring to table 2 and Fig. 3～5.
In Fig. 3～5, be followed successively by health group and the type i diabetes group sample collection of illustrative plates of albumen specific charge peak 7010.32m/z, 3314.94m/z, 7765.57m/z, the red normal group that means, green represents disease group, wherein arrow points is the characteristic protein band of type i diabetes.
The comparison of table 2 Healthy People and type i diabetes protein peak relatively
To all albumen in normal group and type i diabetes group performance difference, the application magnesphere carries out Identification of Fusion Protein.The results are shown in Table 3, table 4.
The qualification result of table 3 and the relevant whole polypeptide of type i diabetes
The peptide identification result of table 4 difference
7010.32,8141.47,7597.7,7645.62,3314.94,8862.88,7765.57,9062.14,7921.58,7832.64,6630.96,4643.76,9289.11,8565.22,3302.42,9429.72,1984.14m/z model is decided to be and adopts 17 input variables like this, is respectively:.The model training discrimination is 100%.And adopting random system of selection to carry out cross validation, the result is 76.86%.Model has good predictive ability.
Table 5 model training result
| Sample | Number of cases | Prediction type i diabetes group | The prediction normal group | Prediction rate % |
| The type i diabetes group | 25 | 25 | 0 | 100 |
| Normal group | 25 | 0 | 25 | 100 |
| Amount to | 50 | 25 | 25 |
To the result of training sample, be as can be seen from Table 5: 25 routine correct judgments in 25 routine normal group, the specificity 100%; 25 routine correct judgments in 25 routine type i diabetes groups, susceptibility is 100%.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the technology of the present invention principle; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (2)
1. set up the method for detection of the mass spectra model of type i diabetes characteristic protein, comprise the steps:
1) serum of collecting the type i diabetes patients serum of many cases clinical definite and normal control personnel is as two groups of serum specimens, carries out cryogenic freezing standby;
2) haemocyanin is carried out to pre-service before mass spectrum;
3) two groups of pretreated haemocyanins are carried out to Mass Spectrometer Method and read, obtain the finger-print of two groups of serum polypeptides;
4) standardization is carried out in the finger-print of all type i diabetes patients and normal human serum polypeptide, and collect data;
5) adopt magnesphere, Nano Aquity UPLC liquid chromatography and LTQ Obitrap XL mass spectrum to identify protein, wherein, peptide section trapping column is
5 μ m, 180 μ m * 20mm; Analytical column is
1.7 μ m, 75 μ m * 150mm; Mobile phase A: 5% acetonitrile, the aqueous solution of 0.1% formic acid; Mobile phase B: 95% acetonitrile, the aqueous solution of 0.1% formic acid; The ratio setting of eluent gradient wash-out is:
Trapping flow velocity 15 μ l/min, trapping time 3min, analyze flow velocity 300nl/min; Analysis time 60min, 35 ℃ of chromatogram column temperatures;
Adopt LTQ Obitrap XL mass spectrometer system, Nano electric spray ion source, spray voltage 1.4kV; Scanning of the mass spectrum time 60min; Experiment model is data dependence and dynamically eliminating, and each parent ion carries out getting rid of 60 seconds after 2 MS/MS; Sweep limit 400-2000m/z; Obitrap is used in one-level scanning, and resolution setting is 60000; LTQ is used in CID and secondary scanning; The single isotope of choosing 10 ions that intensity is the strongest in the MS spectrogram carries out MS/MS as parent ion, and single electric charge is got rid of, not as parent ion;
6) the data obtained is carried out to standardization, filter out 17 type i diabetes characteristic proteins with following mass-to-charge ratio peak: 7010.32,8141.47,7597.7,7645.62,3314.94,8862.88,7765.57,9062.14,7921.58,7832.64,6630.96,4643.76,9289.11,8565.22,3302.42,9429.72 and 1984.14 m/z.
2. the method for claim 1, is characterized in that, wherein step 2) pretreated method is haemocyanin or the polypeptide adopted in magnetic beads for purifying and stable sample.
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| CN102967650A (en) * | 2012-11-29 | 2013-03-13 | 深圳市第二人民医院 | Detection method for characteristic molecules of leukaemia cell protein spectrums |
| CN106324115B (en) * | 2015-07-03 | 2019-02-15 | 中国科学院大连化学物理研究所 | A kind of full ion monitoring quantitative approach based on second order ms |
| CN105738631B (en) * | 2016-02-22 | 2017-10-20 | 西安交通大学 | A kind of autism serum polypeptide mark SERPINA5 A and its application |
| CN105699663A (en) * | 2016-04-06 | 2016-06-22 | 深圳市老年医学研究所 | Salivary protein fingerprint spectrum molecular diagnosis model establishing method of type II diabetes mellitus |
| CN105784829A (en) * | 2016-04-06 | 2016-07-20 | 深圳市老年医学研究所 | Method for establishing finger-print molecular diagnostic model of saliva protein of kidney-deficiency syndrome of type-2 diabetes |
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| CN101398423A (en) * | 2008-11-11 | 2009-04-01 | 毅新兴业（北京）科技有限公司 | Mass spectra model for detecting liver cancer characteristic protein and method for producing same |
| CN101403740A (en) * | 2008-11-13 | 2009-04-08 | 毅新兴业（北京）科技有限公司 | Mass spectrum model used for detecting liver cancer characteristic protein and preparation method thereof |
| WO2011010372A1 (en) * | 2009-07-22 | 2011-01-27 | 株式会社バイオマーカーサイエンス | Method and kit for evaluation of substance, method for screening of substance, and method and kit for determination of presence of occurrence of diabetic nephropathy or potential risk of diabetic nephropathy |
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| CN101398423A (en) * | 2008-11-11 | 2009-04-01 | 毅新兴业（北京）科技有限公司 | Mass spectra model for detecting liver cancer characteristic protein and method for producing same |
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