CN116577403A - Separation detection method and application of exosomes - Google Patents
Separation detection method and application of exosomes Download PDFInfo
- Publication number
- CN116577403A CN116577403A CN202310511926.6A CN202310511926A CN116577403A CN 116577403 A CN116577403 A CN 116577403A CN 202310511926 A CN202310511926 A CN 202310511926A CN 116577403 A CN116577403 A CN 116577403A
- Authority
- CN
- China
- Prior art keywords
- exosome
- exosomes
- titanium dioxide
- ferroferric oxide
- oxide nano
- 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.)
- Pending
Links
- 210000001808 exosome Anatomy 0.000 title claims abstract description 69
- 238000000926 separation method Methods 0.000 title claims abstract description 19
- 238000001514 detection method Methods 0.000 title claims abstract description 16
- GWEVSGVZZGPLCZ-UHFFFAOYSA-N Titan oxide Chemical compound O=[Ti]=O GWEVSGVZZGPLCZ-UHFFFAOYSA-N 0.000 claims abstract description 78
- SZVJSHCCFOBDDC-UHFFFAOYSA-N ferrosoferric oxide Chemical compound O=[Fe]O[Fe]O[Fe]=O SZVJSHCCFOBDDC-UHFFFAOYSA-N 0.000 claims abstract description 39
- 230000002503 metabolic effect Effects 0.000 claims abstract description 39
- 239000004408 titanium dioxide Substances 0.000 claims abstract description 39
- 239000002103 nanocoating Substances 0.000 claims abstract description 34
- 239000004005 microsphere Substances 0.000 claims abstract description 33
- 206010073071 hepatocellular carcinoma Diseases 0.000 claims abstract description 28
- 231100000844 hepatocellular carcinoma Toxicity 0.000 claims abstract description 28
- 238000001840 matrix-assisted laser desorption--ionisation time-of-flight mass spectrometry Methods 0.000 claims abstract description 20
- 239000002207 metabolite Substances 0.000 claims abstract description 20
- 238000010239 partial least squares discriminant analysis Methods 0.000 claims abstract description 9
- 210000002966 serum Anatomy 0.000 claims abstract description 8
- 238000001819 mass spectrum Methods 0.000 claims description 20
- 238000004458 analytical method Methods 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 16
- 238000006243 chemical reaction Methods 0.000 claims description 14
- 150000002500 ions Chemical class 0.000 claims description 12
- LYCAIKOWRPUZTN-UHFFFAOYSA-N Ethylene glycol Chemical compound OCCO LYCAIKOWRPUZTN-UHFFFAOYSA-N 0.000 claims description 10
- 238000003756 stirring Methods 0.000 claims description 10
- 238000004393 prognosis Methods 0.000 claims description 9
- WHBMMWSBFZVSSR-UHFFFAOYSA-N 3-hydroxybutyric acid Chemical compound CC(O)CC(O)=O WHBMMWSBFZVSSR-UHFFFAOYSA-N 0.000 claims description 8
- KZSNJWFQEVHDMF-BYPYZUCNSA-N L-valine Chemical compound CC(C)[C@H](N)C(O)=O KZSNJWFQEVHDMF-BYPYZUCNSA-N 0.000 claims description 8
- 238000003745 diagnosis Methods 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 8
- 238000012549 training Methods 0.000 claims description 8
- 238000005406 washing Methods 0.000 claims description 8
- 238000012353 t test Methods 0.000 claims description 7
- 238000001035 drying Methods 0.000 claims description 6
- YHWCPXVTRSHPNY-UHFFFAOYSA-N butan-1-olate;titanium(4+) Chemical compound [Ti+4].CCCC[O-].CCCC[O-].CCCC[O-].CCCC[O-] YHWCPXVTRSHPNY-UHFFFAOYSA-N 0.000 claims description 5
- 239000008367 deionised water Substances 0.000 claims description 5
- 229910021641 deionized water Inorganic materials 0.000 claims description 5
- 238000001027 hydrothermal synthesis Methods 0.000 claims description 5
- 239000003550 marker Substances 0.000 claims description 5
- 239000002243 precursor Substances 0.000 claims description 5
- 150000003839 salts Chemical class 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- TYEYBOSBBBHJIV-UHFFFAOYSA-N 2-oxobutanoic acid Chemical compound CCC(=O)C(O)=O TYEYBOSBBBHJIV-UHFFFAOYSA-N 0.000 claims description 4
- QHKABHOOEWYVLI-UHFFFAOYSA-N 3-methyl-2-oxobutanoic acid Chemical compound CC(C)C(=O)C(O)=O QHKABHOOEWYVLI-UHFFFAOYSA-N 0.000 claims description 4
- VHUUQVKOLVNVRT-UHFFFAOYSA-N Ammonium hydroxide Chemical compound [NH4+].[OH-] VHUUQVKOLVNVRT-UHFFFAOYSA-N 0.000 claims description 4
- OTZRAYGBFWZKMX-SHSCPDMUSA-N Leukotriene E4 Natural products CCCCCC=C/CC=C/C=C/C=C/C(SCC(N)C(=O)O)C(O)CCCC(=O)O OTZRAYGBFWZKMX-SHSCPDMUSA-N 0.000 claims description 4
- VMHLLURERBWHNL-UHFFFAOYSA-M Sodium acetate Chemical compound [Na+].CC([O-])=O VMHLLURERBWHNL-UHFFFAOYSA-M 0.000 claims description 4
- LFWNPKYGVKNNAB-UHFFFAOYSA-N Trimethylaminoacetone Chemical compound CC(=O)C[N+](C)(C)C LFWNPKYGVKNNAB-UHFFFAOYSA-N 0.000 claims description 4
- 235000011114 ammonium hydroxide Nutrition 0.000 claims description 4
- 239000003153 chemical reaction reagent Substances 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 4
- OTZRAYGBFWZKMX-JUDRUQEKSA-N leukotriene E4 Chemical compound CCCCCC=CCC=C\C=C\C=C\[C@@H](SC[C@H](N)C(O)=O)[C@@H](O)CCCC(O)=O OTZRAYGBFWZKMX-JUDRUQEKSA-N 0.000 claims description 4
- 239000000047 product Substances 0.000 claims description 4
- 229960004295 valine Drugs 0.000 claims description 4
- 229940040526 anhydrous sodium acetate Drugs 0.000 claims description 3
- 150000001768 cations Chemical class 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 239000003960 organic solvent Substances 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 239000012265 solid product Substances 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 claims description 2
- 238000001816 cooling Methods 0.000 claims description 2
- 238000007865 diluting Methods 0.000 claims description 2
- WGCNASOHLSPBMP-UHFFFAOYSA-N hydroxyacetaldehyde Natural products OCC=O WGCNASOHLSPBMP-UHFFFAOYSA-N 0.000 claims description 2
- 238000011534 incubation Methods 0.000 claims description 2
- RBTARNINKXHZNM-UHFFFAOYSA-K iron trichloride Chemical compound Cl[Fe](Cl)Cl RBTARNINKXHZNM-UHFFFAOYSA-K 0.000 claims description 2
- 229910000349 titanium oxysulfate Inorganic materials 0.000 claims description 2
- VXUYXOFXAQZZMF-UHFFFAOYSA-N titanium(IV) isopropoxide Chemical compound CC(C)O[Ti](OC(C)C)(OC(C)C)OC(C)C VXUYXOFXAQZZMF-UHFFFAOYSA-N 0.000 claims description 2
- 229910021578 Iron(III) chloride Inorganic materials 0.000 claims 1
- 238000002955 isolation Methods 0.000 claims 1
- 238000010521 absorption reaction Methods 0.000 abstract description 4
- 239000011159 matrix material Substances 0.000 abstract description 4
- 238000000605 extraction Methods 0.000 abstract description 3
- 238000010606 normalization Methods 0.000 abstract description 3
- 238000011065 in-situ storage Methods 0.000 abstract description 2
- LCKIEQZJEYYRIY-UHFFFAOYSA-N Titanium ion Chemical compound [Ti+4] LCKIEQZJEYYRIY-UHFFFAOYSA-N 0.000 abstract 1
- 230000004907 flux Effects 0.000 abstract 1
- 239000012528 membrane Substances 0.000 abstract 1
- 150000003904 phospholipids Chemical class 0.000 abstract 1
- 238000001269 time-of-flight mass spectrometry Methods 0.000 abstract 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 13
- 238000010200 validation analysis Methods 0.000 description 8
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 7
- 206010028980 Neoplasm Diseases 0.000 description 5
- 238000000692 Student's t-test Methods 0.000 description 5
- 201000010099 disease Diseases 0.000 description 5
- 235000019441 ethanol Nutrition 0.000 description 4
- 238000011528 liquid biopsy Methods 0.000 description 4
- 201000007270 liver cancer Diseases 0.000 description 4
- 208000014018 liver neoplasm Diseases 0.000 description 4
- 238000004949 mass spectrometry Methods 0.000 description 4
- 239000002105 nanoparticle Substances 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- 201000011510 cancer Diseases 0.000 description 3
- 238000003795 desorption Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- NQXWGWZJXJUMQB-UHFFFAOYSA-K iron trichloride hexahydrate Chemical group O.O.O.O.O.O.[Cl-].Cl[Fe+]Cl NQXWGWZJXJUMQB-UHFFFAOYSA-K 0.000 description 3
- 210000004185 liver Anatomy 0.000 description 3
- 238000010197 meta-analysis Methods 0.000 description 3
- 239000002077 nanosphere Substances 0.000 description 3
- 230000035945 sensitivity Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000001291 vacuum drying Methods 0.000 description 3
- 208000005443 Circulating Neoplastic Cells Diseases 0.000 description 2
- 238000003917 TEM image Methods 0.000 description 2
- 238000000862 absorption spectrum Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 2
- 239000011324 bead Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 238000007621 cluster analysis Methods 0.000 description 2
- 230000034994 death Effects 0.000 description 2
- 231100000517 death Toxicity 0.000 description 2
- 238000013399 early diagnosis Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000000338 in vitro Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000004060 metabolic process Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002504 physiological saline solution Substances 0.000 description 2
- 238000000634 powder X-ray diffraction Methods 0.000 description 2
- 206010061818 Disease progression Diseases 0.000 description 1
- 206010016654 Fibrosis Diseases 0.000 description 1
- 206010062016 Immunosuppression Diseases 0.000 description 1
- 206010027476 Metastases Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 239000003463 adsorbent Substances 0.000 description 1
- 230000001476 alcoholic effect Effects 0.000 description 1
- 102000013529 alpha-Fetoproteins Human genes 0.000 description 1
- 108010026331 alpha-Fetoproteins Proteins 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 210000001124 body fluid Anatomy 0.000 description 1
- 239000010839 body fluid Substances 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000007882 cirrhosis Effects 0.000 description 1
- 208000019425 cirrhosis of liver Diseases 0.000 description 1
- 239000011258 core-shell material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000005750 disease progression Effects 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000010494 dissociation reaction Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000001506 immunosuppresive effect Effects 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 208000019423 liver disease Diseases 0.000 description 1
- 238000001254 matrix assisted laser desorption--ionisation time-of-flight mass spectrum Methods 0.000 description 1
- 230000009401 metastasis Effects 0.000 description 1
- 239000002102 nanobead Substances 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 238000010837 poor prognosis Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 102000004196 processed proteins & peptides Human genes 0.000 description 1
- 108090000765 processed proteins & peptides Proteins 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000001959 radiotherapy Methods 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 210000003296 saliva Anatomy 0.000 description 1
- 238000001878 scanning electron micrograph Methods 0.000 description 1
- 230000000405 serological effect Effects 0.000 description 1
- 235000017281 sodium acetate Nutrition 0.000 description 1
- 239000001632 sodium acetate Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000007790 solid phase Substances 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 210000001519 tissue Anatomy 0.000 description 1
- 238000002054 transplantation Methods 0.000 description 1
- 210000002700 urine Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
- G01N27/626—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas
- G01N27/628—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas and a beam of energy, e.g. laser enhanced ionisation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change
Landscapes
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Optics & Photonics (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a separation detection method and application of exosomes. The invention utilizes magnetic ferroferric oxide nano-microspheres coated with titanium dioxide nano-coatings to separate and enrich exosomes in serum, uses a matrix-assisted laser ionization desorption-time-of-flight mass spectrometry to acquire metabolite fingerprint of exosomes, performs characteristic extraction and normalization on the metabolite fingerprint, and performs orthogonal partial least squares discriminant analysis to screen metabolic markers and distinguish healthy people from hepatocellular carcinoma patients. The magnetic ferroferric oxide nano microsphere has high-valence titanium ion load and can act with a phospholipid membrane of an exosome, so that the exosome is separated, and the excellent ultraviolet absorption characteristic ensures efficient in-situ MALDI-TOF MS detection. The exosome separation detection method and the exosome separation detection system can accurately and efficiently distinguish healthy people from hepatocellular carcinoma patients, are low in cost and high in flux, consume less samples, and have wide application prospects.
Description
Technical Field
The invention relates to a separation detection method of exosomes and application thereof, in particular to a method for separating and detecting exosomes by using magnetic ferroferric oxide nano microspheres coated with titanium dioxide nano-coatings, which is applied to screening potential exosome characteristic metabolic markers of liver cancer and diagnosis and prognosis monitoring of liver cancer diseases, and belongs to the technical field of biological detection.
Background
Cancer is the second leading cause of death worldwide, resulting in about ten million deaths each year, most of which are not diagnosed until metastasis. In this case, large-scale screening and early diagnosis are key issues for accurate prevention and treatment. Liquid biopsies are characterized by being much less invasive and associated with less risk and complications than tissue biopsies involving surgery or needle procedures. With the benefit of this, liquid biopsies can be performed more frequently to provide more dynamic images of the physical state, allowing real-time monitoring of disease progression and timely response to treatment. The hot targets of current liquid biopsies include circulating tumor cells, circulating tumor DNA and exosomes. Exosomes carry a variety of biomolecules, such as proteins, peptides, lipids, metabolites, and nucleic acids, providing molecular characterization information of tumors and corresponding microenvironments. Furthermore, exosomes are easier to detect compared to very low concentrations of circulating tumor cells. In addition, the exosomes are more widespread and are found in various body fluids, such as blood, urine and saliva. Therefore, exosome-based liquid biopsies play an important role in the large-scale screening and early diagnosis of diseases, especially cancer.
Hepatocellular carcinoma is a cancer that is ubiquitous worldwide and is often present in cirrhosis patients, an important risk factor that leads to high mortality worldwide. Unfortunately, hepatocellular carcinoma has a high incidence and a generally poor prognosis, making it a challenging disease requiring effective management. To date, the main treatment for hepatocellular carcinoma has remained liver resection and transplantation, but these options are severely limited by donor shortages, high cost and the burden of lifelong immunosuppression. Many clinical cases of hepatocellular carcinoma remain asymptomatic at an early stage due to liver compensation, whereas the traditional serological marker alpha fetoprotein of hepatocellular carcinoma has been shown to show low accuracy, greatly impairing its reliability. Recently, the use of liver-derived exosomes as markers for hepatocellular carcinoma has become of increasing interest, as they have been found to be closely related to liver diseases including hepatocellular carcinoma. This finding underscores the potential of exosomes as important resources for developing more accurate markers of hepatocellular carcinoma.
Currently, matrix-assisted laser desorption/ionization mass spectrometry is a popular technique for large-scale complex sample metabolic analysis because of its high throughput, high sensitivity, short test time, low sample consumption, and versatility of different samples. For metabolic analysis of matrix-assisted laser desorption/ionization mass spectrometry, previous experience has shown that it is critical to construct a suitable inorganic matrix that needs to be able to avoid background interference of self-dissociation of conventional organic matrices and at the same time enhance selective ionization/desorption of metabolites.
Disclosure of Invention
The purpose of the invention is that: aiming at the defects of the existing liver cancer detection technology, the invention provides a separation detection method and application of exosomes, the method utilizes magnetic ferroferric oxide nano microspheres coated with titanium dioxide nano coatings to separate exosomes, and performs in-situ mass spectrometry detection, and has the advantage of rapid magnetic response, and the titanium dioxide nano coatings and exosomes have strong interaction, so that the method can separate and detect exosomes more rapidly, more sensitively and more selectively, and has higher clinical value and good application prospect when being applied to diagnosis and prognosis monitoring of liver cancer diseases.
In order to achieve the above object, the present invention provides a separation and detection method for exosomes, comprising the steps of:
step 1: separating exosomes in the sample by using the magnetic ferroferric oxide nano microspheres coated with the titanium dioxide nano coating to obtain magnetic nano microspheres combined with the exosomes;
step 2: and (3) dispersing the magnetic nano-microspheres combined with the exosomes obtained in the step (1) in deionized water to prepare an analysis solution, and carrying out MALDI-TOF MS analysis.
Preferably, the preparation method of the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating used in the step 1 comprises the following steps:
step 1.1: dissolving ferric salt in glycol, adding anhydrous sodium acetate after the solution is clear and transparent, fully stirring and ultrasonically transferring the solution into a reaction kettle for hydrothermal reaction, cooling the reaction kettle to room temperature after the reaction is finished, collecting and washing the obtained product, and then drying to obtain the magnetic ferroferric oxide nano microsphere;
step 1.2: and (3) uniformly dispersing the magnetic ferroferric oxide nano-microspheres obtained in the step (1.1) and concentrated ammonia water in an organic solvent, adding a titanium dioxide precursor, stirring for reaction, collecting and washing a solid product after the reaction is finished, and drying to obtain the magnetic ferroferric oxide nano-microspheres coated with the titanium dioxide nano-coating.
Preferably, the ferric salt in the step 1.1 is ferric trichloride and/or hydrate thereof; the temperature of the hydrothermal reaction is 150-250 ℃ and the time is 10-20 hours.
More preferably, the ferric salt is ferric trichloride hexahydrate, and the dosage ratio of the ferric trichloride hexahydrate, the ethylene glycol and the anhydrous sodium acetate is 1.3-1.4 g: 70-80 mL: 3-4 g.
Preferably, the drying conditions in the step 1.1 and the step 1.2 are as follows: vacuum drying at 40-60 deg.c.
Preferably, the titanium dioxide precursor in the step 1.2 is tetrabutyl titanate, isopropyl titanate, titanyl sulfate, tiCl 4 And TiCl 3 At least one of (a) and (b); the conditions of the stirring reaction are as follows: stirring and reacting for 20-30 hours at 40-50 ℃.
More preferably, the titanium dioxide precursor is tetrabutyl titanate and the organic solvent is an alcoholic solvent, most preferably ethanol.
More preferably, the dosage ratio of the concentrated ammonia water, the ethanol and the tetrabutyl titanate is 0.6-0.8 mL: 150-250 mL: 1-2 mL.
Preferably, the step 1 specifically includes: diluting a sample with normal saline, adding the magnetic ferroferric oxide nano microspheres coated with the titanium dioxide nano coating for co-incubation, and finally washing to obtain the magnetic spheres combined with exosomes; the sample is subject serum.
Preferably, the specific conditions for the MALDI-TOF MS analysis in the step 2 are as follows: adopting a 355nmND:YAG laser source, wherein the laser frequency is 2000Hz, the accelerating voltage is 20kV, the voltage at the ion source 1 is 20kV, and the voltage at the ion source 2 is 17.6kV; the collection mode is a reflection cation mode, and the collection range m/z is 100-1000Da; each sample was tested in 3 replicates; mass spectral data were obtained from flexControl 3.4 and data were derived in flexAnalysis 3.4.
The invention also provides application of the exosomes obtained by the exosome separation detection method in preparation of hepatocellular carcinoma diagnostic reagents and prognosis monitoring reagents.
Preferably, the application is based on MALDI-TOF MS analysis of batch samples to obtain exosome metabolite fingerprint and based on the fingerprint, orthogonal partial least squares discriminant analysis of the selected characteristic metabolic markers.
Preferably, the specific conditions for acquiring the exosome metabolite fingerprint spectrum are as follows: normalizing mass spectrum peak intensity by using total ion current for mass spectrograms obtained by carrying out MALDI-TOF MS analysis on a batch of samples, averaging the mass spectrograms of each sample, aligning the tolerance of peak coordinates to 0.4, and listing the mass spectrum peak intensity of each sample to obtain a metabolite fingerprint; the batch of samples comprises a training set and a verification set;
and/or, the screening step of the characteristic metabolic marker comprises: and (3) performing orthogonal partial least squares discriminant analysis on the sample training set by using Metaboanalysis 5.0 and SIMCA-P14.1, calculating t-test P value, VIP value, P (cov, x-axis) and P (corr, y-axis) value of each mass spectrum peak, screening the mass spectrum peaks with t-test P <0.05, VIP value >3, P (cov, x-axis) > |0.05|, and P (corr, y-axis) > |0.5|to obtain the metabolite corresponding to the mass spectrum peak, namely the characteristic metabolic marker.
The invention also provides a hepatocellular carcinoma diagnosis system or prognosis monitoring system, which comprises an exosome separation device and a matrix-assisted laser desorption ionization time-of-flight mass spectrometer for analyzing exosome metabolic substances, wherein the exosome separation device is provided with magnetic ferroferric oxide nano microspheres coated with a titanium dioxide nano coating;
the matrix-assisted laser desorption ionization time-of-flight mass spectrometer is used for acquiring a MALDI-TOF MS mass spectrum of exosome metabolic substances in a subject sample, judging whether the subject suffers from hepatocellular carcinoma or not based on the peak intensities of characteristic metabolic markers in the mass spectrum, wherein in the mass spectrum, if the peak intensities of the trimethylaminoacetone, the alpha-ketoisovaleric acid, the L-valine, the 2-ketobutyric acid and the 3-hydroxybutyric acid are obviously higher than a normal level (more than 2 times standard deviation), the peak intensities of the leukotriene E4 are obviously lower than the normal level (more than 2 times standard deviation), judging that the subject suffers from hepatocellular carcinoma under 95% confidence, and if the peak intensities of the 6 characteristic metabolic markers do not obviously change from the normal level (not more than 2 times standard deviation), judging that the subject does not suffer from hepatocellular carcinoma under 95% confidence;
or the matrix-assisted laser desorption ionization time-of-flight mass spectrometer monitors MALDI-TOF MS mass spectrograms of exosome metabolites in samples before and after treatment of a hepatocellular carcinoma patient, and evaluates the prognosis effect (operation and/or radiotherapy treatment effect) based on the peak intensity change level of characteristic metabolic markers in the mass spectrograms, and if the peak intensities corresponding to 6 characteristic metabolic markers contained in the mass spectrograms tend to be at normal levels, the prognosis effect is considered to be better.
Wherein the peak intensity is normalized intensity, and in the mass spectrum, the abscissa represents the mass-to-charge ratio (m/z) value of the ion; the ordinate represents the intensity of the ion flow, usually expressed in normalized intensity, i.e. the total ion flow intensity is defined as 100%, and the intensity of each peak ion flow is expressed as a percentage thereof.
The magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating can effectively separate exosomes through quick magnetic response and strong interaction, exosome components can be ionized efficiently due to strong ultraviolet absorption and high conductivity, exosome metabolite fingerprint is successfully extracted, 6 metabolic markers with obvious differences are screened out by means of a machine learning algorithm, quick in-vitro diagnosis of hepatocellular carcinoma is realized, and the method has high sensitivity and high accuracy, so that the method has wide application prospect in the aspects of large-scale crowd screening, disease diagnosis and prognosis monitoring.
Compared with the prior art, the invention has the beneficial effects that:
(1) The exosome separation detection method provided by the invention utilizes the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating, has rapid magnetic response, and has strong interaction with the exosome, so that the exosome can be separated more rapidly, sensitively and selectively;
(2) The strong ultraviolet absorption and high conductivity of the titanium dioxide nano coating are beneficial to absorbing laser energy and ionizing exosome components, so that the method can efficiently ionize the exosome metabolites obtained by separation, and generate remarkable mass spectrum signals;
(3) The method provided by the invention can be used for identifying the exosome metabolism difference between a patient with hepatocellular carcinoma and a normal person through a machine learning algorithm, distinguishing serum samples of the hepatocellular carcinoma and the normal person with high accuracy and sensitivity, screening out 6 metabolic markers with obvious difference, and successfully utilizing the 6 metabolic markers to realize rapid in-vitro diagnosis of the hepatocellular carcinoma.
Drawings
FIG. 1 is a scanning electron micrograph of magnetic ferroferric oxide nanospheres coated with a titanium dioxide nanocoating of example 1;
FIG. 2 is a transmission electron micrograph of magnetic ferroferric oxide nanospheres coated with a titanium dioxide nanocoating of example 1;
FIG. 3 is an X-ray powder diffraction pattern of magnetic ferroferric oxide nanobeads coated with titanium dioxide nanocoating of example 1;
FIG. 4 is an ultraviolet-visible absorption spectrum of magnetic ferroferric oxide nanoparticle coated with titanium dioxide nanocoating of example 1;
FIG. 5 is a fingerprint spectrum of serum exosome metabolism obtained from magnetic ferroferric oxide nanospheres coated with titanium dioxide nanocoating of example 2;
FIG. 6 is a graph of the orthogonal partial least squares discriminant analysis score based on a validation set of example 3;
FIG. 7 is a cluster analysis heatmap based on validation set of example 3;
fig. 8 is a training set and validation set based receiver characteristic curve of example 4.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Example 1: and (3) synthesizing the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating.
(1) After 1.35g of ferric trichloride hexahydrate is magnetically stirred in 75mL of ethylene glycol until the solid is completely dissolved, adding 3.6g of sodium acetate, fully stirring and ultrasonically transferring into a hydrothermal reaction kettle, heating at 200 ℃ for 16 hours, after the reaction kettle is cooled, collecting a solid product, respectively washing the product three times by deionized water and ethanol, and vacuum drying at 50 ℃ to obtain the magnetic ferroferric oxide nano microsphere;
(2) And (3) uniformly dispersing 80mg of the magnetic ferroferric oxide nano-microsphere obtained in the step (1) and concentrated ammonia water in absolute ethyl alcohol, dropwise adding tetrabutyl titanate, stirring at 45 ℃ for 24 hours, washing the product three times by deionized water and ethanol respectively after the reaction is finished, and carrying out vacuum drying at 50 ℃ to obtain the magnetic ferroferric oxide nano-microsphere coated with the titanium dioxide nano-coating.
The scanning electron microscope photograph of the prepared magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating is shown in figure 1; a transmission electron micrograph of the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating is shown in figure 2; an X-ray powder diffraction spectrum of the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating is shown in figure 3; the ultraviolet-visible absorption spectrum of the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating is shown in figure 4.
Analysis results: as can be seen from fig. 1 and 2, the magnetic ferroferric oxide nano-microsphere coated with the titanium dioxide nano-coating has a uniform spherical morphology and a core-shell structure, and has a diameter of about 200 nanometers; from fig. 3, it can be seen that the magnetic ferroferric oxide nanoparticle coated with the titanium dioxide nano-coating has excellent crystallinity; from fig. 4, it can be seen that the magnetic ferroferric oxide nanoparticle coated with the titanium dioxide nano-coating has excellent absorption intensity at the wavelength of the laser light source.
Example 2: the magnetic ferroferric oxide nano microsphere coated with the titanium dioxide nano coating obtained in the example 1 is used as a solid phase adsorbent for separating and detecting exosomes in serum of 50 hepatocellular carcinoma patients and serum of 46 healthy human blood.
(1) The sera of 50 hepatocellular carcinoma patients and 46 healthy controls were randomly divided into training and validation sets at a ratio of 4:1, corresponding to 77 and 19 samples, respectively.
(2) The sample was diluted 20 times with physiological saline, 2mg of the magnetic ferroferric oxide nanoparticle coated with the titanium dioxide nanocoating was added, incubated at 25 ℃ for 15 minutes, the magnetic beads were washed with physiological saline, and the magnetic beads bound to the exosomes were dispersed in deionized water at a concentration of 10 mg/mL.
(3) Mass spectrometry: taking 1 mu L of the dispersion liquid in the step (2), carrying out MALDI-TOF MS analysis after natural drying, adopting Bruker ultrafleXtreme MALDI-TOF mass spectrum, adopting a 355nm Nd: YAG laser source, wherein the laser frequency is 2000Hz, the acceleration voltage is 20kV, the voltage at the ion source 1 is 20kV, and the voltage at the ion source 2 is 17.6kV; the collection mode is a reflection cation mode, and the collection range m/z is 100-1000Da; each sample was tested in 3 replicates; mass spectral data were obtained from flexControl 3.4 and data were derived in flexAnalysis 3.4. The mass spectrum is shown in fig. 5.
Analysis results: as can be seen from fig. 5, the exosome metabolite fingerprint obtained by separating the magnetic ferroferric oxide nano-microspheres coated with the titanium dioxide nano-coating contains rich information, and the disease and health metabolite fingerprint has good differentiation and potential for being used as a diagnosis basis.
Example 3: peak extraction and normalization were performed on the serum exosome metabolite spectra obtained in example 2, orthogonal partial least squares discriminant analysis was performed on the training set using metaanalysis 5.0 and SIMCA-P14.1, t-test P-value, VIP-value, P (cov, x-axis), P (corr, y-axis) value for each mass spectrum peak were calculated, and characteristic metabolic markers were screened.
(1) Peak extraction and normalization of exosome metabolic profile was performed using R package MALDIquant, MALDIquantForeign.
(2) Orthogonal partial least squares discriminant analysis was performed on the training set using metaanalysis 5.0 and SIMCA-P14.1 to select for characteristic metabolic markers. Specifically, the t-test P value, the VIP value, the P (cov, x-axis) and the P (corr, y-axis) of each mass spectrum peak are calculated by using Metaboanalysis 5.0 and SIMCA-P14.1, the metabolites corresponding to the mass spectrum peaks of which the t-test P <0.05, the VIP value >3, P (cov, x-axis) > |0.05|, P (corr, y-axis) > |0.5| are screened out as characteristic metabolic markers, and 6 characteristic metabolic markers, namely, trimethylaminoacetone (m/z 138.08), alpha-ketoisovaleric acid (m/z 138.89), L-valine (m/z 140.06), 2-ketobutyric acid (m/z 140.90), 3-hydroxybutyric acid (m/z 142.91) and leukotriene E4 (m/z 462.00) are screened out.
(3) And performing cluster analysis on the verification set based on the characteristic metabolic markers.
The score of the validation set samples was plotted using SIMCA 14.1 and the cluster heatmap of the validation set samples was plotted using metaanalysis 5.0. The verification set score graph is shown in fig. 6; the verification set cluster heat map is shown in fig. 7.
Analysis results: as can be seen from fig. 6, the discrimination based on the screened metabolic markers in the orthorhombic partial least squares discriminant analysis model is good. As can be seen from fig. 7, the discrimination of the validation set based on the screened metabolic markers is good, and the disease and health samples are each clustered together.
Example 4: the 6 feature metabolic markers obtained in example 3 were used to make a classification prediction for the training set and the validation set. Samples were classified into healthy and diseased (hepatocellular carcinoma) samples based on peak intensities of the characteristic metabolic markers, if the peak intensities corresponding to trimethyl amino acetone, α -ketoisovaleric acid, L-valine, 2-ketobutyric acid, 3-hydroxybutyric acid were significantly higher than normal (more than 2 standard deviations), the peak corresponding to leukotriene E4 was significantly lower than normal (more than 2 standard deviations), the samples were classified as hepatocellular carcinoma samples at 95% confidence, and if the peak intensities corresponding to 6 characteristic metabolic markers were not significantly changed from normal (not more than 2 standard deviations), the samples were classified as healthy samples at 95% confidence. The standard deviation is calculated based on the peak intensities of the characteristic metabolic markers corresponding to 46 healthy controls in the sample, and the normal level is the average value of the peak intensities of the characteristic metabolic markers corresponding to 46 healthy controls in the sample. The receiver characteristic is shown in fig. 8.
Analysis results: as can be seen from fig. 8, the classification prediction accuracy based on the characteristic metabolites is very high, the area under the curve reaches 1, and the screened characteristic metabolic markers are proved to have practicability in diagnostic application.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to be limiting in any way and in nature, and it should be noted that several modifications and additions may be made to those skilled in the art without departing from the invention, which modifications and additions are also intended to be construed as within the scope of the invention.
Claims (10)
1. The separation and detection method of the exosomes is characterized by comprising the following steps:
step 1: separating exosomes in the sample by using the magnetic ferroferric oxide nano microspheres coated with the titanium dioxide nano coating to obtain magnetic spheres combined with the exosomes;
step 2: and (3) dispersing the magnetic spheres combined with the exosomes obtained in the step (1) in deionized water to prepare an analysis solution, and carrying out MALDI-TOF MS analysis.
2. The method for detecting the separation of exosomes according to claim 1, wherein the method for preparing magnetic ferroferric oxide nano-microspheres coated with titanium dioxide nano-coating used in the step 1 comprises the following steps:
step 1.1: dissolving ferric salt in glycol, adding anhydrous sodium acetate after the solution is clear and transparent, fully stirring and ultrasonically transferring the solution into a reaction kettle for hydrothermal reaction, cooling the reaction kettle to room temperature after the reaction is finished, collecting and washing the obtained product, and then drying to obtain the magnetic ferroferric oxide nano microsphere;
step 1.2: and (3) uniformly dispersing the magnetic ferroferric oxide nano-microspheres obtained in the step (1.1) and concentrated ammonia water in an organic solvent, adding a titanium dioxide precursor, stirring for reaction, collecting and washing a solid product after the reaction is finished, and drying to obtain the magnetic ferroferric oxide nano-microspheres coated with the titanium dioxide nano-coating.
3. The method according to claim 2, wherein the ferric salt in step 1.1 is ferric chloride and/or a hydrate thereof; the temperature of the hydrothermal reaction is 150-250 ℃ and the time is 10-20 hours.
4. The method for detecting the separation of exosomes according to claim 2, wherein the titanium dioxide precursor in step 1.2 is tetrabutyl titanate, isopropyl titanate, titanyl sulfate, tiCl 4 And TiCl 3 At least one of (a) and (b); the conditions of the stirring reaction are as follows: stirring and reacting for 20-30 hours at 40-50 ℃.
5. The method for detecting the separation of exosomes according to claim 1, wherein step 1 specifically comprises: diluting a sample with normal saline, adding the magnetic ferroferric oxide nano microspheres coated with the titanium dioxide nano coating for co-incubation, and finally washing to obtain the magnetic spheres combined with exosomes; the sample is subject serum.
6. The method according to claim 1, wherein the specific conditions for MALDI-TOF MS analysis in step 2 are: adopting a 355nm Nd:YAG laser source, wherein the laser frequency is 2000Hz, the accelerating voltage is 20kV, the voltage at the ion source 1 is 20kV, and the voltage at the ion source 2 is 17.6kV; the collection mode is a reflection cation mode, and the collection range m/z is 100-1000Da; each sample was tested in 3 replicates; mass spectral data were obtained from flexControl 3.4 and data were derived in flexAnalysis 3.4.
7. Use of an exosome obtained by the method for isolation detection of an exosome according to any one of claims 1 to 6 for the preparation of a diagnostic reagent and a prognostic monitoring reagent for hepatocellular carcinoma.
8. The use according to claim 7, wherein the use is based on a batch of samples for MALDI-TOF MS analysis of acquired exosome metabolite fingerprints and based on said fingerprints for orthorhombic partial least squares discriminant analysis of screened characteristic metabolic markers.
9. The use according to claim 8, wherein the specific conditions for obtaining the exosome metabolite fingerprint are: normalizing mass spectrum peak intensity by using total ion current for mass spectrograms obtained by carrying out MALDI-TOF MS analysis on a batch of samples, averaging the mass spectrograms of each sample, aligning the tolerance of peak coordinates to 0.4, and listing the mass spectrum peak intensity of each sample to obtain a metabolite fingerprint;
and/or, the screening step of the characteristic metabolic marker comprises: orthogonal partial least squares discriminant analysis is carried out on the training set by using Metaboanalysis 5.0 and SIMCA-P14.1, t test P value, VIP value, P (cov, x-axis) and P (corr, y-axis) value of each mass spectrum peak are calculated, and metabolites corresponding to mass spectrum peaks of t test P <0.05, VIP value >3, P (cov, x-axis) > |0.05|, P (corr, y-axis) > |0.5| are screened to obtain the characteristic metabolic marker.
10. The hepatocellular carcinoma diagnosis or prognosis monitoring system is characterized by comprising an exosome separation device and a matrix-assisted laser desorption ionization time-of-flight mass spectrometer for analyzing exosome metabolic substances, wherein the exosome separation device is provided with magnetic ferroferric oxide nano microspheres coated with a titanium dioxide nano coating;
the matrix-assisted laser desorption ionization time-of-flight mass spectrometer is used for acquiring a MALDI-TOF MS mass spectrum of exosome metabolic substances in a subject sample and judging whether the subject has hepatocellular carcinoma or not based on the peak intensity of characteristic metabolic markers in the mass spectrum;
or the matrix-assisted laser desorption ionization time-of-flight mass spectrometer monitors MALDI-TOF MS mass spectrograms of exosome metabolic substances in samples before and after the treatment of the hepatocellular carcinoma patient, and evaluates the prognosis effect based on the peak intensity change level of characteristic metabolic markers in the mass spectrograms;
the characteristic metabolic markers are trimethylaminoacetone, alpha-ketoisovaleric acid, L-valine, 2-ketobutyric acid, 3-hydroxybutyric acid and leukotriene E4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310511926.6A CN116577403A (en) | 2023-05-08 | 2023-05-08 | Separation detection method and application of exosomes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310511926.6A CN116577403A (en) | 2023-05-08 | 2023-05-08 | Separation detection method and application of exosomes |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116577403A true CN116577403A (en) | 2023-08-11 |
Family
ID=87533398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310511926.6A Pending CN116577403A (en) | 2023-05-08 | 2023-05-08 | Separation detection method and application of exosomes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116577403A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117511879A (en) * | 2024-01-04 | 2024-02-06 | 北京理工大学 | Method for integrally realizing exosome enrichment and micromolecule extraction based on microfluidic chip |
-
2023
- 2023-05-08 CN CN202310511926.6A patent/CN116577403A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117511879A (en) * | 2024-01-04 | 2024-02-06 | 北京理工大学 | Method for integrally realizing exosome enrichment and micromolecule extraction based on microfluidic chip |
| CN117511879B (en) * | 2024-01-04 | 2024-05-03 | 北京理工大学 | Method for integrally realizing exosome enrichment and micromolecule extraction based on microfluidic chip |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109884302B (en) | Lung cancer early diagnosis marker based on metabonomics and artificial intelligence technology and application thereof | |
| CN109946390B (en) | Lung cancer diagnosis marker combination and application | |
| CN110646554B (en) | Pancreatic cancer diagnosis marker based on metabonomics and screening method and application thereof | |
| Hu et al. | Discovery of oral fluid biomarkers for human oral cancer by mass spectrometry | |
| WO2020259187A1 (en) | Rapid tumor tissue identification method based on fingerprint spectrogram of lipids on tissue surface | |
| Morse et al. | Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies | |
| US8653006B2 (en) | Metabolite biomarkers for the detection of esophageal cancer using NMR | |
| CN111562338B (en) | Application of transparent renal cell carcinoma metabolic marker in renal cell carcinoma early screening and diagnosis product | |
| EP2550533A1 (en) | Early detection of recurrent breast cancer using metabolite profiling | |
| CN116577403A (en) | Separation detection method and application of exosomes | |
| WO2013071677A1 (en) | Compound fingerprint atlas-spectrum model used for early gastric cancer diagnosis/early-warning, and model establishing | |
| CN111458399B (en) | Mass spectrum detection method for low-molecular-weight substances based on palladium-gold core-shell micro-nano material | |
| KR20210108899A (en) | Cancer diagnosis using Raman signal of urine | |
| CN114540491B (en) | Liver cancer prediction model establishment and application based on differential expression miRNA in fucosylation extracellular vesicles | |
| CN106324072B (en) | Application of iron oxide matrix in cerebrospinal fluid mass spectrometry | |
| US20140162903A1 (en) | Metabolite Biomarkers For Forecasting The Outcome of Preoperative Chemotherapy For Breast Cancer Treatment | |
| CN112986441A (en) | Tumor marker screened from tissue metabolism contour, application thereof and auxiliary diagnosis method | |
| CN110568196B (en) | Metabolic marker related to low-grade glioma in urine and application thereof | |
| Jeon et al. | Histology‐directed matrix‐assisted laser desorption/ionization analysis reveals tissue origin and p53 status of primary liver cancers | |
| Norris et al. | Imaging mass spectrometry in clinical pathology | |
| CN116413432A (en) | Pancreatic cancer diagnosis marker based on intestinal flora metabonomics, screening method and application thereof | |
| CN115472293A (en) | Lung adenocarcinoma multiomic diagnosis model based on serum metabolic fingerprint and construction method thereof | |
| CN112834652B (en) | Acute aortic dissection patient-specific biomarker composition and application thereof | |
| US20190391157A1 (en) | Mass spectrometry imaging of benign melanocytic nevi and malignant melanomas | |
| Li et al. | SiO 2@ Au nanoshell-assisted laser desorption/ionization mass spectrometry for coronary heart disease diagnosis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |