CN110530965A - A kind of method that silicon nanowire array chip detects cell metabolite, lipid - Google Patents
A kind of method that silicon nanowire array chip detects cell metabolite, lipid Download PDFInfo
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- CN110530965A CN110530965A CN201910947984.7A CN201910947984A CN110530965A CN 110530965 A CN110530965 A CN 110530965A CN 201910947984 A CN201910947984 A CN 201910947984A CN 110530965 A CN110530965 A CN 110530965A
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- G—PHYSICS
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- 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
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- H01L21/0271—Making masks on semiconductor bodies for further photolithographic processing not provided for in group H01L21/18 or H01L21/34 comprising organic layers
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- H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
- H01L21/04—Manufacture or treatment of semiconductor devices or of parts thereof the devices having potential barriers, e.g. a PN junction, depletion layer or carrier concentration layer
- H01L21/48—Manufacture or treatment of parts, e.g. containers, prior to assembly of the devices, using processes not provided for in a single one of the subgroups H01L21/06 - H01L21/326
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Abstract
The invention discloses the methods of a kind of silicon nanowire array chip detection cell metabolite, lipid, the present invention obtains the silicon nanowire array chip with multiple sites by lithographic patterning and metal auxiliary etch, then is surface modified again on the basis of the silicon nanowire array chip in multiple sites；Obtained chip is by superior desorption ability, ionizability and exempts from the mass spectrographic high-throughput detectability collection of matrix and is integrated, and simplifies the pretreatment and detection process of complex samples；Again by establishing spectrum analysis model, the cell subsets of cell sample, easy to operate after enabling this method to detect single cell sample, cell mixing sample, xenogenesis implantation tumor tissue samples and drug effect simultaneously, has a wide range of application.
Description
Technical field
The present invention relates to Mass Spectrometer Method field, especially a kind of silicon nanowire array chip detects cell metabolite, lipid
Method.
Background technique
Metabolism group is one of the group received significant attention at present in clinical and scientific research field, as genomics and egg
The downstream product of Bai Zuxue reflects the real-time metaboilic level variation of cell.Existing clinical study results show many diseases such as
Cancer, Parkinson, alzheimer, obesity etc. are related to lesion cell metabolism disorder, therefore examine to the metabolin of cell
Surveying analysis can help to find potential disease marker, be expected to be applied to drug screening and push the development of precisely medical treatment.Base
Matter Assisted Laser Desorption ionization mass spectrometry (MALDI-MS) has high-throughput, highly sensitive, high cover as a kind of novel soft ionization mass spectrum
The advantages that lid and the tolerance of salinity are high, is widely used in the detection and analysis of protein, polypeptide and polymer of biological sample etc., still
By organic substrate lower than the background interference within the scope of 800Da small molecule, MALDI metabolism analyte detection application aspect by
Limitation.
In consideration of it, some nano materials with good charge and energy-handling capability, especially semiconductor nano material,
Such as silicon materials, metal oxide, metal sulfide etc., the surface that be used to that MALDI matrix be replaced to be directly used in small molecule are auxiliary
The detection for helping laser desorption/ionization mass spectrometry (SALDI-MS), avoid organic substrate low molecular weight area signal interference (<
800Da).SALDI-MS detection in, in order to keep the high-throughput detectability of MALDI and improve Mass Spectrometer Method operation stabilization
Property, ideal nanostructure substrate should be the chip containing multiple separation detection sites of adaptation mass spectrometer.Patent
It is the multidigit lattice array core for cheating bottom material that CN205691542U, which is formed by substrate of silicon wafer with bellmouth and gold, silver nano particle,
Piece can exempt from the glutathione in matrix detection biological sample.Patent CN207689423U glass slide is substrate, is formed on surface
Multiple pits simultaneously combine metal nanoparticle as matrix in pit, can exempt from matrix and detect within the scope of m/z100-300
Molecular signal.Although these array target plates have an ability of more site primer small molecules, it is at high cost, test object is single,
Detection molecules amount range is small, detects poor repeatability.Therefore, above-mentioned technology is not in the metabolism of cell sample and actual clinical sample
It is applied in analyte detection.Development cost is low, small-molecular-weight detection range is wide, can be used for the high-throughput mass spectrum chip of cellular identification
It is very necessary.
Market needs one kind that can simplify the pretreatment and detection process of complex samples, and can detect multiple types simultaneously
The Mass Spectrometry detection method of the cell subsets of sample, the present invention solve such problems.
Summary of the invention
To solve the deficiencies in the prior art, the purpose of the present invention is to provide a kind of silicon nanowire array chips to detect cell
The method of metabolin, lipid, by the nanostructure characteristic and characteristic of semiconductor using silicon nanowires chip, by silicon nanowires core
The superior desorption ability of piece, ionizability and exempt from the mass spectrographic high-throughput detectability collection of matrix and be integrated, simplifies complexity
The pretreatment and detection process of sample；Again by establishing spectrum analysis model, this method is enabled to detect single cell simultaneously
The cell subsets of cell sample after sample, cell mixing sample, xenogenesis implantation tumor tissue samples and drug effect, operation letter
It is single, have a wide range of application.
In order to achieve the above objectives, the present invention adopts the following technical scheme that:
A kind of method that silicon nanowire array chip detects cell metabolite, lipid, including following content:
Step 1 prepares silicon nanowire array chip；
Step 2 obtains cell sample to be detected and is transferred on chip, obtains under laser desorption/ionization mass spectrometry platform
Cell metabolite, lipid mass spectrogram；
Step 3 establishes the discrimination model of various kinds of cell hypotype in conjunction with statistical analysis technique, to the hypotype of each cell into
Row differentiates and distinguishes；
Step 4 carries out hypotype to cell mixing sample, tumor tissues sample in conjunction with the cell subsets discrimination model of foundation
Differentiate and distinguishes.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, includes the following steps:
Step 1, prepared silicon receive linear array chip；
Step 2, obtains cell sample to be detected and is transferred on chip, and chip is fixed on MALDI-MS instrument
On target holder and enter sample storehouse, after selecting detection pattern, laser energy and detection molecules amount range, to marking in detection range
Quasi-molecule amount correction, successively on acquisition chip each site mass spectrogram；Detection pattern includes: negative ion mode and cation mould
Formula；
Step 3 is analyzed, it is established that can distinguish a variety of in conjunction with cell mass spectrogram of the statistical analysis technique to acquisition
The discrimination model of cell subsets；
Detailed process are as follows:
A, the raw mass spectrum data that will acquire are exported with ASCII fromat, are set molecular information by mathematical software and are extracted journey
Sequence obtains original spectrogram in the mass-to-charge ratio for setting all peaks in molecular weight ranges and corresponding strength information；
Spectrogram detection range is divided into multiple cluster peaks according to peak shape distribution and similar mass-to-charge ratio, carried out in each cluster peak by b
Inside normalization；
Obtained relative intensity information is filtered out characteristic peak after univariate statistics analysis and multivariate statistical analysis by c
Cluster, and for showing each intercellular significant difference and carrying out cell subsets differentiation；By every kind of sub-types of cells initial data,
Normalization data and feature cluster peak data differentiate database base as hypotype according to its sub-type identity classified and stored in cluster peak
Plinth；
Step 4, the sample database building obtained with step 3 differentiates and prediction model, and data with existing sample is called to use
In the differentiation and differentiation of unknown cell/tissue sample.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 1, prepared silicon nanometer
The method of linear array chip are as follows: by monocrystalline silicon piece after surface washing, had by lithographic patterning and metal auxiliary etch
There is the silicon nanowire array chip in multiple sites, then surface chemistry is carried out to the silicon nanowire array chip with multiple sites and is repaired
Decorations are nanometer-material-modified.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 2 obtain to be detected
Cell sample is simultaneously transferred into the method on chip are as follows: contains mlCell suspended solution by liquid-liquid extraction method is isolated
Have extract cell metabolite, lipid organic phase solution, then by organic phase solution be dried with nitrogen weight it is molten after obtain sample to be tested,
Sample to be tested is taken to drip in the sample spot on silicon nano-array chip, wait spontaneously dry.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 2 obtain to be detected
Cell sample is simultaneously transferred into the method on chip are as follows: the concentration detected by the cell count of collection and needed for being diluted to obtains
The cell suspending liquid arrived takes sample to be tested to drip in the sample spot on silicon nano-array chip directly as sample to be tested, to
It spontaneously dries.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 2 obtain to be detected
Cell sample is simultaneously transferred into the method on chip are as follows:
Silicon nanowire array chip and the PDMS film of corresponding punching are bonded to each other after oxygen plasma treatment, are obtained
Chip is incubated for using the porous cell of silicon nanowire array as hole bottom, PDMS as hole wall；
Cell is transferred in the hole of cell incubation chip, so that cell is adhered fully to bottom by the incubation of suitable time
Silicon nanowires above；
The cell in every hole is cleaned using cleaning agent, throws off the duct PDMS of silicon nanowire array chip surface
The silicon nanowire array chip of film, array surface adherent cell is completely dried.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 3, c, by what is obtained
Relative intensity information filters out characteristic peak cluster after univariate statistics analysis and multivariate statistical analysis, and each thin for showing
The significant difference of intercellular and carry out cell subsets differentiation, by normalization data in every kind of sub-types of cells initial data, cluster peak with
And feature cluster peak data differentiates Basis of Database as hypotype according to its sub-type identity classified and stored；
Univariate statistics analysis includes: that t is examined, and variance analysis, Mann-Whitney U is examined；
Multivariate statistical analysis includes: PCA principal component analysis, HCL hierarchical cluster analysis, SOM Self-organizing Maps, LDA line
Property discriminant analysis, PLS-DA ginsenoside, the orthogonal Partial Least Squares discriminant analysis of OPLS-DA, PC-DA
Principal component discriminant analysis.
The method that a kind of silicon nanowire array chip above-mentioned detects cell metabolite, lipid, step 4, with obtained sample
Database building differentiates and prediction model, and data with existing sample is called to be used for the differentiation and prediction of unknown cell/tissue sample；
Differentiate and predictive ability assessed by calculating sensibility, specificity, accuracy rate and Pearson correlation coefficient,
Show that model has if sensibility, specificity, accuracy rate value are greater than 90% and Pearson correlation coefficient value is greater than 0.8 good to sentence
Other and predictive ability；
Differentiate that with prediction model include: artificial neural network, genetic algorithm, support vector machines.
The invention has the beneficial effects that:
The present invention obtains the silicon nanowire array chip with multiple sites by lithographic patterning and metal auxiliary etch,
Such chip is for good energy, charge accumulated and transfer ability, and under laser action, silicon nanowires is by photoelectricity efficiency shadow
Sound can make the molecule on top desorb and ionize, and can not be generated to avoid the background appearance of base material by controlling laser energy
Background interference is to realize the small molecule detection exempted under matrix condition；
It is surface modified again on the basis of the silicon nanowire array chip in multiple sites；Obtained chip has excellent
Small molecule detectability, surface can make by chemical modification and with metal, metal oxide, the compound of carbon nanomaterial
It has the function to certain class molecule enhancing detection in biological sample, and the characteristic that can lead to target detection thing carries out silicon nanowires core
The optimization and selection of piece；
The present invention enables this method to detect single cell sample, mixing simultaneously carefully by establishing spectrum analysis model
The cell subsets of cell sample, easy to operate, application range after born of the same parents' sample, xenogenesis implantation tumor tissue samples and drug effect
Extensively.
Detailed description of the invention
Fig. 1 is the silicon nanowire array chip schematic diagram in the present invention, and surface is the sample lattice array of 6*8, each sample
Spot diameter is 3mm, spacing 1.8mm；
Fig. 2 is the scanning electron microscope (SEM) photograph of silicon nanowire array on the silicon nanowire array chip in the present invention, and (a) is literalness
The silicon nanowires battle array of silicon nanowire array surface (b) metal nanoparticle modification silicon nanowire array surface (c) graphene modified
List face；
Fig. 3 is to use the silicon nanowire array chip in the present invention as substrate, the 5 kinds of liver cancer obtained according to Mass Spectrometer Method
The metabolin of cell, lipid spectral data by data analysis extract 4 groups of feature cluster peak datas analyze (a) PCA distinguish as a result,
It is obviously distinguished between 5 kinds of liver cancer cells, no juxtaposition, PC1 0.5555, PC2 0.1908, PC3 0.1555, three
Person's addition and value is 0.9018；(b) HCA cluster result, accuracy rate 100%；
Fig. 4 is using the silicon nanowire array in the present invention be substrate detection cell mixing sample (LM3:Huh7=3:1,
1:1,1:3 are successively labeled as LM3-t, L3H1-t, L1H3-t, Huh7-t in figure) metabolin, lipid spectral data is by number
4 groups of feature cluster peak datas, which are extracted, according to analysis does HCA analysis result；
Fig. 5 is using the silicon nanowire array in the present invention be substrate detection xenogenesis implantation tumor tissue samples (LM3:
Huh7=1:0,3:1,1:3,0:1) metabolin, lipid spectral data by data analysis extract 4 groups of feature cluster peak datas and 5
Group model cell does the result of Pearson correlation coefficient analysis；
It is that substrate detects 3 kinds of Sorafenib effects 24 for not having to concentration that Fig. 6, which is using the silicon nanowire array in the present invention,
Metabolin, the lipid spectral data of LM3 cell afterwards are analyzed by data extracts 4 groups of feature cluster peak datas and 5 group model cells
Do the result of linear discriminant analysis (LDA)；
Fig. 7 is the LDA X-Y scheme experimental result of experiment five of the present invention.
Specific embodiment
Specific introduce is made to the present invention below in conjunction with the drawings and specific embodiments.
A kind of method that silicon nanowire array chip detects cell metabolite, lipid, including following content:
Step 1 prepares silicon nanowire array chip；
Prepare the method for silicon nanowire array chip are as follows: by monocrystalline silicon piece after surface washing, by lithographic patterning and
Metal auxiliary etch obtains the silicon nanowire array chip with multiple sites, such as the literalness silicon nanowire array table of Fig. 2 (a)
Shown in face, then surface chemical modification or nanometer-material-modified is carried out to the silicon nanowire array chip with multiple sites；
Patterned lattice array is manufactured in silicon chip surface by photoetching process, dot matrix is etched by metal Assisted Chemical Etching Process method
Silicon nanowire array chip is prepared in column, and the diameter of sample spot is 1-3mm in each array, and silicon nanowires length is 0.5-3 μ
M, linear dimension 50-200nm.
Surface chemical modification includes: alkyl modified, amido modified, carboxyl modified；Nanometer-material-modified includes: metal nano
Particle modification, metal oxide nanoparticles modification, carbon nanomaterial modification.
Metal nanoparticle modifies embodiment 1: silicon nanowire array chip is removed surface oxidation through low concentration HF solution
It after layer, immerses in low-concentration metallic salting liquid and reacts 1-10min, the silicon nanowires for having metal nanoparticle to surface is obtained after washing
Array chip.
Metal nanoparticle modifies embodiment 2: silicon nanowire array chip through low concentration hydrofluoric acid except after surface oxide layer,
One layer of metal oxide precursor solution of surface spin coating, at room temperature spontaneously dry after under the conditions of 450 DEG C high-temperature process 2h, obtain
There is the silicon nanowire array chip of metal nanoparticle or metal oxide nanoparticles modification on surface.
Figure (b) metal nanoparticle such as is obtained to modify shown in silicon nanowire array surface.
Carbon nanomaterial modification: the solution containing carbon nanomaterial is schemed, the method for spin coating by drop coating, leaching, carbon is made to receive
Rice material is adsorbed in surface of silicon nanowires of the surface with amino group, obtains the silicon nanowires core for having carbon nanomaterial to modify to surface
Piece is as shown in the silicon nanowire array surface of Fig. 2 (c) graphene modified.
Surface chemical modification embodiment 1: by resulting silicon nanowire array chip through oxygen plasma treatment exposed surface Si-
10- is reacted in dry environments with the toluene solution of the siloxanes of the inactive group of the amino-containing siloxanes of end group or end after OH
120min obtains end with the silicon nanowire array chip containing amino or containing alkyl chain.
Surface chemical modification embodiment 2: resulting silicon nanowire array chip is removed into surface oxidation through low concentration hydrofluoric acid
Layer after with the toluene solution of undecenoic acid or acrylic acid under anaerobic anhydrous condition back flow reaction 1-2h, undecenoic acid or third
The volumetric concentration of olefin(e) acid is 2%-10%.It is successively washed with toluene, ethyl alcohol after reaction, surface can be obtained with alkyl chain
And end is the silicon nanowire array chip of carboxyl.
It should be understood that lithographic patterning and metal auxiliary etch obtain the silicon nanowire array core with multiple sites
The surface modification of piece and chip had been described in the patent before our company, and details are not described herein.
Step 2, obtains cell sample to be detected and is transferred on chip, and chip is fixed on MALDI-MS instrument
On target holder and enter sample storehouse, after selecting detection pattern, laser energy and detection molecules amount range, to marking in detection range
Quasi-molecule amount correction, successively on acquisition chip each site mass spectrogram.Detection pattern includes: negative ion mode and cation mould
Formula.
Specific method embodiment 1 are as follows: mlCell suspended solution is contained into extraction cell by liquid-liquid extraction method is isolated
The organic phase solution of metabolin, lipid, then by organic phase solution be dried with nitrogen weight it is molten after obtain sample to be tested, take sample to be tested
It drips in the sample spot on silicon nano-array chip, Mass Spectrometer Method is carried out after spontaneously drying.
Specific method embodiment 2 are as follows: the concentration detected by the cell count of collection and needed for being diluted to, obtained cell are outstanding
Supernatant liquid takes sample to be tested to drip in the sample spot on silicon nano-array chip, after spontaneously drying directly as sample to be tested
Carry out Mass Spectrometer Method.
Specific method embodiment 3 are as follows:
The PDMS film of a, silicon nanowire array chip and corresponding punching is bonded to each other after oxygen plasma treatment, is obtained
Chip is incubated for the porous cell using silicon nanowire array as hole bottom, PDMS as hole wall；
Cell is transferred in the hole of cell incubation chip with suitable concentration, keeps cell complete by the incubation of suitable time by b
It adheres to above the silicon nanowires of bottom entirely；
C cleans the cell in every hole using cleaning agent, throws off the duct PDMS of silicon nanowire array chip surface
Film, the silicon nanowire array chip of array surface adherent cell carry out Mass Spectrometer Method after being completely dried.
These three embodiments are verified in following experiment one.
Step 3 is analyzed, it is established that can distinguish a variety of in conjunction with cell mass spectrogram of the statistical analysis technique to acquisition
The discrimination model of cell subsets.
Detailed process are as follows:
The raw mass spectrum data that a will acquire are exported with ASCII fromat, are set molecular information by mathematical software and are extracted journey
Sequence obtains original spectrogram in the mass-to-charge ratio for setting all peaks in molecular weight ranges and corresponding strength information.
Spectrogram detection range is divided into multiple cluster peaks according to peak shape distribution and similar mass-to-charge ratio by b, is carried out in each cluster peak
Inside normalization.
Obtained relative intensity information is filtered out characteristic peak after univariate statistics analysis and multivariate statistical analysis by c
Cluster, and for showing different intercellular significant differences and carrying out cell subsets differentiation.By every kind of sub-types of cells initial data,
Normalization data and feature cluster peak data differentiate database base as hypotype according to its sub-type identity classified and stored in cluster peak
Plinth.
Univariate statistics analysis includes: that t is examined, and variance analysis, Mann-Whitney U is examined；
Univariate statistics Analysis and Screening characteristic peak standard: value < 0.05 P is to have significant difference.
Multivariate statistical analysis includes: PCA principal component analysis, HCL hierarchical cluster analysis, SOM Self-organizing Maps, LDA line
Property discriminant analysis, PLS-DA ginsenoside, the orthogonal Partial Least Squares discriminant analysis of OPLS-DA, PC-DA
Principal component discriminant analysis.
Step 4, the sample database building obtained with step 3 differentiates and prediction model, and data with existing sample is called to use
In the differentiation and differentiation of unknown cell/tissue sample.
Differentiate and predictive ability assessed by calculating sensibility, specificity, accuracy rate and Pearson correlation coefficient,
Show that model has if sensibility, specificity, accuracy rate value are greater than 90% and Pearson correlation coefficient value is greater than 0.6 good to sentence
Other and predictive ability.
Differentiate that with prediction model include: artificial neural network, genetic algorithm, support vector machines.
Experiment one, the method that verifying obtains cell sample to be detected are suitable for the present invention；
Embodiment 1, the mass spectrum based on silicon nanowire array chip directly detect the tool of cell metabolite, lipid in suspension
Body step are as follows: the cell of collection is washed for several times and counted using PBS, is diluted to 10^5-10^8/mL's using PBS after the completion
MlCell suspended solution, obtained cell suspending liquid take 1-5 μ L to drip on silicon nano-array chip directly as sample to be tested
In sample spot, chip is fixed on mass spectrum target plate after spontaneously drying, detection obtains metabolism point under positive or negative ion mode
Sub- spectrogram information.
Embodiment 2, metabolin, the rouge of the direct detection chip surface adhering cells of mass spectrum based on silicon nanowire array chip
The specific steps of matter an are as follows: chip size is identical with silicon nanowires chip, correspond to the battle array on chip with a thickness of the PDMS film of 3-6mm
The PDMS film of column position and size punching, silicon nanowire array chip and corresponding punching is mutual after oxygen plasma treatment
Fitting obtains being incubated for chip using silicon nanowire array as hole bottom, PDMS as the porous cell of hole wall.By cell to be suitable for
Concentration is transferred in the hole of cell incubation chip, is adhered fully to cell on the silicon nanowires of bottom by the incubation of 12-24h
Face.The cell in every hole is cleaned using PBS, throws off the PDMS porthole die of silicon nanowire array chip surface, is obtained every
It is stained with the silicon nanowire array chip of cell on a array point, is fixed to after being completely dried on mass spectrum target plate, just
Or detection obtains metabolic molecule spectrogram information under negative ion mode.
The step of embodiment 3, the Mass Spectrometer Method based on silicon nanowire array chip passes through the cell metabolite extracted, lipid
Specifically: the mlCell suspended solution that concentration is 10^5-10^8/mL is extracted into the generation separated in cell by liquid-liquid extraction method
Object, lipid are thanked, the use of solvent is methanol: water: chloroform=1:1:2, is centrifuged after high speed is vortexed concussion 5min in 6000r/min
10min, the chloroform layer containing cell metabolite and lipid are located at lower layer, by obtaining the chlorine containing extracting substance after drawing separation
Imitative solution, through being dried with nitrogen methanol weight it is molten after as sample to be tested, take 1-5 μ L to drip to the sample spot on silicon nano-array chip
On, chip is fixed on mass spectrum target plate after spontaneously drying, detection obtains metabolic molecule spectrogram under positive or negative ion mode
Information.It includes deionized water, methanol, acetonitrile, acetone, isopropanol, methyl tertbutyl that extractant can be used in liquid-liquid extraction
Ether, chloroform, methylene chloride etc..
Experiment two, establishes cell by different liver cancer cells metabolins, the lipid of silicon nanowire array chip Detection and Extraction
The step of hypotype identification model are as follows:
By experiment one embodiment 1 the step of detect respectively 5 kinds of liver cancer cells (LM3, HepG2, Hep3B, HA22T,
Huh7 lipid) expresses spectrogram, and detection obtains every kind of cytolipin spectrogram each 30, the mass spectrometric data of acquisition is converted to
Ascii data form imports in mathematical software and reads out all effective peaks (S/N > 3).By the effective peak of all spectrograms according to peak shape
Distribution and similar mass-to-charge ratio are divided into 20 cluster peaks, intensity normalization are carried out in cluster peak, as subsequent single argument and multivariable
The basic data of data analysis.
Pass through student ' s t-test and 5 groups of cell data are subjected to t inspection Fenix, filters out with significant difference
Characteristic peak cluster (value < 0.05 P), one shares 4 groups of cluster peaks is screened out as feature cluster peak, mass-to-charge ratio distribution be m/z
697-702, m/z771-781, m/z 857-864 and m/z 902-914.Extract this 4 groups of cluster peak numbers in all cell spectrograms
According to by principal component analysis (PCA), stratiform clustering (HCA) etc. verifies the hypotype separating capacity at feature cluster peak, at PCA points
It shows as being brought together from the data of 5 kinds of cells are each in analysis, intersect without the data point from not allogenic cell, and
Preceding 3 principal component addition and value > 0.85.The data from allogenic cell are shown as in HCA is collected as a major class, different cells
Between data without intersection, as shown in Figure 4.
Experiment three, establishes cell by different liver cancer cells metabolins, the lipid of silicon nanowire array chip Detection and Extraction
The step of hypotype identification model are as follows:
Detect LM3 the and Huh7 mixing with cells sample of different mixing proportion respectively by the step of testing one embodiment 1
The lipid of (3:1,1:1,1:3) expresses spectrogram, and detection obtains the lipid spectrogram of each mixing sample, and the mass spectrometric data of acquisition is turned
It is changed to ascii data form, imports in mathematical software and reads out all effective peaks (S/N > 3).By the effective peak of all spectrograms according to
Peak shape distribution and similar mass-to-charge ratio are divided into 20 cluster peaks, carry out intensity normalization in cluster peak.Extract in all spectrograms with
The corresponding data in 4 groups of feature cluster peaks in experiment two, the predominant cell hypotype in cell mixing is judged by HCA.Judgment basis
For the similarity height for showing as same class object in HCA, it can be seen that LM3:Huh7=3:1 mixing sample is shown and LM3
The high similitude of cell, LM3:Huh7=1:3 mixing sample show with the high similitude of Huh7 cell, LM3:Huh7=1:1 is very
Sample mixing is originally more similar to the biggish Huh7 cell of unicellular volume, as shown in Figure 5.
Experiment four plants liver cancer tissue metabolin, lipid by the different xenogenesis of silicon nanowire array chip Detection and Extraction
The step of establishing cell subsets identification model are as follows:
Remove counting and resuspending step from, detection is mixed from different injections respectively by the step of testing one embodiment 1
The lipid of composition and division in a proportion example cell sample (LM3:Huh7=1:0,3:1,1:3,0:1) expresses spectrogram, and detection obtains each tissue sample
The mass spectrometric data of acquisition is converted to ascii data form, read out in importing mathematical software all effective by this lipid spectrogram
Peak (S/N > 3).The effective peak of all spectrograms is divided into 20 cluster peaks according to peak shape distribution and similar mass-to-charge ratio, is carried out in cluster peak
Intensity normalization.Data corresponding with 4 groups of feature cluster peaks in experiment two in all spectrograms are extracted, artificial neural network is passed through
Analysis and Pearson correlation coefficient analyze and determine the predominant cell hypotype of tissue samples.In artificial neural network analysis, setting
Training set is the 70% of all data, and verifying collection is 15%, and test set 15%, model is automatically to prediction sensibility, specificity
It is calculated with accuracy rate.Obtained 3 groups of tissue samples (LM3:Huh7=1:0,3:1,1:3) as the result is shown show and LM3
The strong correlation of cell, susceptibility, specificity and accuracy rate reach 100%.LM3:Huh7=1:3 tissue samples are shown as
The strong correlation of LM3 be derived from LM3 cell competitive growth ability more stronger than Huh7 cell, be grown to tumor tissues finally with
Based on LM3.The tissue samples of LM3:Huh7=0:1 are shown as the strong correlation with Huh7 cell, susceptibility, specificity and accurate
Rate reaches 100%.As shown in Fig. 6 and table 1, it is that substrate detects xenogenesis that table 1, which is using the silicon nanowire array in the present invention,
Metabolin, the lipid spectral data of implantation tumor tissue samples (LM3:Huh7=1:0,3:1,1:3,0:1) are analyzed by data
It extracts 4 groups of feature cluster peak datas and 5 group model cells does artificial neural network analysis result.
Table 1
The cell subsets that the results show experiment 2 is established differentiates and prediction model may be equally applied to tissue samples.
Experiment five is established thin by cell metabolite, lipid after the drug effect of silicon nanowire array chip Detection and Extraction
The step of born of the same parents' hypotype identification model are as follows:
The LM3 that 3 groups of various concentration Sorafenibs are incubated for 24 hours altogether is detected respectively by the step of testing one embodiment 1
The lipid of cell expresses spectrogram, and detection obtains the lipid spectrogram of each cell sample, the mass spectrometric data of acquisition is converted to ASCII
Data mode imports in mathematical software and reads out all effective peaks (S/N > 3).By the effective peak of all spectrograms according to peak shape distribution and
Similar mass-to-charge ratio is divided into 20 cluster peaks, carries out intensity normalization in cluster peak.Extract in all spectrograms with experiment two in 4
The corresponding data in group feature cluster peak, carry out linear discriminant analysis LDA.It is highest according to contribution rate according to the data point of all cells
First linear discriminent and the second linear discriminent obtain LDA X-Y scheme, if between the LM3 cell focus point in experiment two
Distance be less than with other 4 kinds of intercellular distances, then be identified as LM3 cell；If with experiment two in LM3 cell focus point it
Between distance the distance between be greater than with other 4 kinds of cell samples, then differentiate not it is LM3 cell.3 groups of drug effects as the result is shown
LM3 cell in experiment is completely coincident in LDA with the LM3 cell in experiment two, as shown in Figure 7.Illustrate discrimination model
The high accuracy and applicability differentiate to drug effect cell subsets.
The present invention provides a kind of method that silicon nanowire array chip detects cell metabolite, lipid, by being received using silicon
The nanostructure characteristic and characteristic of semiconductor of rice noodles chip, by the superior desorption ability of silicon nanowires chip, ionizability and
Exempt from the mass spectrographic high-throughput detectability collection of matrix to be integrated, simplifies the pretreatment and detection process of complex samples；Pass through again
Spectrum analysis model is established, this method is enabled to detect single cell sample, cell mixing sample, xenogenesis implantation tumor simultaneously
The cell subsets of cell sample, easy to operate after tissue samples and drug effect, has a wide range of application.
The basic principles, main features and advantages of the invention have been shown and described above.The technical staff of the industry should
Understand, the above embodiments do not limit the invention in any form, all obtained by the way of equivalent substitution or equivalent transformation
Technical solution is fallen within the scope of protection of the present invention.
Claims (8)
1. a kind of method of silicon nanowire array chip detection cell metabolite, lipid, which is characterized in that including following content:
Step 1 prepares silicon nanowire array chip；
Step 2 obtains cell sample to be detected and is transferred on chip, obtains cell under laser desorption/ionization mass spectrometry platform
Metabolin, lipid mass spectrogram；
Step 3 establishes the discrimination model of various kinds of cell hypotype in conjunction with statistical analysis technique, sentences to the hypotype of each cell
Not and distinguish；
Step 4 carries out hypotype differentiation to cell mixing sample, tumor tissues sample in conjunction with the cell subsets discrimination model of foundation
And differentiation.
2. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 1, lipid, feature
It is, includes the following steps:
Step 1 prepares silicon nanowire array chip；
Step 2 obtains cell sample to be detected and is transferred on chip, and chip is fixed on MALDI-MS instrument target holder
Sample storehouse is gone up and enters, after selecting detection pattern, laser energy and detection molecules amount range, to carrying out standard scores in detection range
Son amount correction, successively on acquisition chip each site mass spectrogram；The detection pattern includes: negative ion mode and cation mould
Formula；
Step 3 is analyzed, it is established that can distinguish various kinds of cell in conjunction with cell mass spectrogram of the statistical analysis technique to acquisition
The discrimination model of hypotype；
Detailed process are as follows:
A, the raw mass spectrum data that will acquire are exported with ASCII fromat, are set molecular information extraction procedure by mathematical software, are obtained
Take original spectrogram in the mass-to-charge ratio for setting all peaks in molecular weight ranges and corresponding strength information；
Spectrogram detection range is divided into multiple cluster peaks according to peak shape distribution and similar mass-to-charge ratio, carried out in each cluster peak internal by b
Normalization；
Obtained relative intensity information is filtered out characteristic peak cluster after univariate statistics analysis and multivariate statistical analysis by c, and
For showing each intercellular significant difference and carrying out cell subsets differentiation；By every kind of sub-types of cells initial data, cluster peak
Interior normalization data and feature cluster peak data differentiate Basis of Database as hypotype according to its sub-type identity classified and stored；
Step 4, the sample database building obtained with step 3 differentiates and prediction model, calls data with existing sample for not
Know the differentiation and differentiation of cell/tissue sample.
3. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 1, prepares the method for silicon nanowire array chip are as follows: by monocrystalline silicon piece after surface washing, pass through photoengraving pattern
Change and metal auxiliary etch obtains the silicon nanowire array chip with multiple sites, then to the silicon nanowires with multiple sites
Array chip carries out surface chemical modification or nanometer-material-modified.
4. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 2, obtains cell sample to be detected and is transferred into the method on chip are as follows: passes through mlCell suspended solution
Liquid-liquid extraction method is isolated containing the organic phase solution for extracting cell metabolite, lipid, then by organic phase solution nitrogen
Sample to be tested is obtained after drying weight is molten, takes sample to be tested to drip in the sample spot on silicon nano-array chip, wait spontaneously dry.
5. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 2, obtains cell sample to be detected and is transferred into the method on chip are as follows: by the cell count of collection and dilute
The concentration detected needed for being interpreted into, obtained cell suspending liquid take sample to be tested to drip to silicon nanometer battle array directly as sample to be tested
In sample spot on column chip, wait spontaneously dry.
6. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 2, obtain cell sample to be detected and is transferred into the method on chip are as follows:
Silicon nanowire array chip and the PDMS film of corresponding chip site punching are bonded to each other after oxygen plasma treatment,
It obtains being incubated for chip using silicon nanowire array as hole bottom, PDMS as the porous cell of hole wall；
Cell is transferred in the hole of cell incubation chip, is adhered fully to cell on the silicon nanowires of bottom by incubation
Face；
The cell in every hole is cleaned using cleaning agent, throws off the PDMS porthole die of silicon nanowire array chip surface, battle array
The silicon nanowire array chip of column surface adhering cells is completely dried.
7. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 3, c, obtained relative intensity information is filtered out into spy after univariate statistics analysis and multivariate statistical analysis
Peak cluster is levied, and for showing each intercellular significant difference and carrying out cell subsets differentiation, every kind of sub-types of cells is original
Normalization data and feature cluster peak data differentiate data as hypotype according to its sub-type identity classified and stored in data, cluster peak
Library basis；
The univariate statistics analysis includes: that t is examined, and variance analysis, Mann-Whitney U is examined；
The multivariate statistical analysis includes: PCA principal component analysis, HCL hierarchical cluster analysis, SOM Self-organizing Maps, LDA line
Property discriminant analysis, PLS-DA ginsenoside, the orthogonal Partial Least Squares discriminant analysis of OPLS-DA, PC-DA
Principal component discriminant analysis.
8. the method for a kind of silicon nanowire array chip detection cell metabolite according to claim 2, lipid, feature
It is, step 4, is differentiated with obtained sample database building and prediction model, calling data with existing sample are used for unknown thin
The differentiation of born of the same parents/tissue samples and prediction；
Differentiate and predictive ability is assessed by calculating sensibility, specificity, accuracy rate and Pearson correlation coefficient, if quick
Perception, specificity, accuracy rate value be greater than 90% and Pearson correlation coefficient value be greater than 0.8 show model have it is good differentiation and
Predictive ability；
The differentiation and prediction model include: artificial neural network, genetic algorithm, support vector machines.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111413395A (en) * | 2020-04-21 | 2020-07-14 | 四川大学 | Application of porous silicon nanowire combined with MA L DI-TOF MS in metabolic small molecule detection |
| CN112461915A (en) * | 2020-12-08 | 2021-03-09 | 杭州汇健科技有限公司 | Method for characteristic molecule screening, drug sensitivity degree detection and cell subtype detection related to drug sensitivity |
| CN113533492A (en) * | 2021-07-16 | 2021-10-22 | 浙江大学 | Laser desorption ionization mass spectrometry kit for rapidly detecting small molecular substances in liquid sample and using method thereof |
| CN114264719A (en) * | 2021-12-31 | 2022-04-01 | 杭州汇健科技有限公司 | Biological sample lipid dual-mode detection mass spectrum chip, kit and application |
| CN115144519A (en) * | 2022-06-30 | 2022-10-04 | 上海交通大学 | Single cell sample fingerprint detection method based on inorganic nanoparticles and application |
| CN116106396A (en) * | 2023-04-13 | 2023-05-12 | 杭州汇健科技有限公司 | Full spectrum fitting dynamic correction method and device for mass spectrum data, medium and mass spectrometer |
| CN118090564A (en) * | 2024-04-23 | 2024-05-28 | 青岛大学 | Rapid urinary tract infection detection method based on single-cell optical fluctuation method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070224697A1 (en) * | 2006-03-24 | 2007-09-27 | Bruker Daltonics, Inc. | Means and method for analyzing samples by mass spectrometry |
| CN109541012A (en) * | 2018-11-23 | 2019-03-29 | 杭州汇健科技有限公司 | A kind of universal nano chips and the preparation method and application thereof for mass spectral analysis |
| CN110243921A (en) * | 2019-06-28 | 2019-09-17 | 浙江大学 | A kind of Rapid tumor organization discrimination method based on tissue surface lipid fingerprint chromatogram |
-
2019
- 2019-10-08 CN CN201910947984.7A patent/CN110530965B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070224697A1 (en) * | 2006-03-24 | 2007-09-27 | Bruker Daltonics, Inc. | Means and method for analyzing samples by mass spectrometry |
| CN109541012A (en) * | 2018-11-23 | 2019-03-29 | 杭州汇健科技有限公司 | A kind of universal nano chips and the preparation method and application thereof for mass spectral analysis |
| CN110243921A (en) * | 2019-06-28 | 2019-09-17 | 浙江大学 | A kind of Rapid tumor organization discrimination method based on tissue surface lipid fingerprint chromatogram |
Non-Patent Citations (4)
| Title |
|---|
| E.P.GO ET AL.: "Desorption/Ionization on Silicon Nanowires", 《ANAL.CHEM.》 * |
| GEON KOOK LEE ET AL.: "Liqid MALDI profile classifies non-small cell lung cancers according to the histologic type", 《LUNG CANCER》 * |
| 周庆涛: "《半导体纳米线及其阵列可控生长和物性研究》", 31 October 2012, 合肥工业大学出版社 * |
| 王方丽等: "基于纳米材料的表面辅助激光解吸离子化质谱研究", 《化学进展》 * |
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