CN114264719A - Biological sample lipid dual-mode detection mass spectrum chip, kit and application - Google Patents
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Abstract
The invention discloses a mass spectrum chip combination or a mass spectrum chip for performing dual-mode detection on a biological sample lipid extract based on a mass spectrum method, and belongs to the field of mass spectrum detection. The mass spectrum chip combination or the mass spectrum chip comprises a sample application area formed by different silicon nano structures, is used for receiving a biological sample lipid extract, and is used for scanning in a positive ion mode and a negative ion mode respectively, so as to screen data in the same detection mode, and select mass spectrum data with the widest peak number coverage range for statistical analysis. The mass spectrum chip combination or the mass spectrum chip for the lipid dual-mode detection of the biological sample is used for lipid detection, so that the background interference in a lipid detection range can be reduced, the high coverage rate and the detection sensitivity of the lipid and metabolites in a positive ion mode and a negative ion mode can be considered, the number of detected effective peaks can be increased to 479, and the effective peaks contain neutral lipid and polar lipid.
Description
Technical Field
The invention belongs to the technical field of mass spectrometry detection, and particularly relates to a mass spectrometry chip, a kit and application for dual-mode detection of biological sample lipid, and in particular relates to a mass spectrometry chip, a kit and application for dual-mode detection of biological sample lipid based on a mass spectrometry method.
Background
Cancer is one of the public health problems of global concern. Over 60% of countries in 2019 have cancer as the first or second leading cause of death in humans. Furthermore, based on the cancer predictions of the world health organization, it is expected that the number of cancer patients will increase by 47% worldwide by 2040 years. Therefore, how to perform early noninvasive diagnosis of cancer by biomarkers is still one of the most promising research fields at present. At present, the tumor marker used for noninvasive diagnosis clinically is few, the sensitivity is low (60-70 percent), and the sensitivity and the specificity of diagnosis cannot be well balanced. However, the omics markers developed in recent years have been receiving much attention because they have excellent diagnostic ability by combination of various markers.
So-called omics studies typically include genomics, transcriptome, proteomics, and metabolomics, of which lipidomics are a branch. Because the lipid participates in various cell activities and device composition, the change of the lipid directly reflects the energy balance, structure and functionalization of cells, thereby being well related to disease phenotype and being beneficial to the excavation of disease markers. Among a plurality of research objects of lipidomics, serum is a preferred object for body fluid lipid analysis as a body fluid which is easily obtained clinically and contains abundant lipid information.
At present, most of serum lipid is analyzed under an SALDI-MS platform by adopting a single nano material and being limited in one mode, and the high coverage rate and the detection sensitivity of lipid and metabolites under the positive ion mode and the negative ion mode cannot be considered simultaneously. For example, Kai Liang et al developed a method of analyzing serum lipids in a positive ion mode using graphene oxide material polymers (AGO) to discriminate hyperlipidemia patients from healthy controls (Liang, K.; Gao, H.; Gu, Y.; Yang, S.; Zhang, J.; Li, J.; Wang, Y.; Li, Y.Anal.Chim.acta 2018,1035, 108-. PDA-modified anti-reflective materials prepared by lacing Yang et al were also analyzed for serum lipids only in the positive ion mode (Yang, J.; Zhang, W.; Zhang, H.; Zhong, M.; Cao, W.; Li, Z.; Huang, X.; Nie, Z.; Liu, J.; Li, P.; Ma X.; Ouyang, Z. ACS Appl Mater Interfaces 2019,11, 46140-46148.). However, for the diagnosis and analysis of diseases, the lipid data acquisition method with high coverage rate is more beneficial to the statistical analysis and the construction of an excellent diagnosis model.
Disclosure of Invention
Aiming at the problems that the serum lipid is mostly analyzed by adopting a single nano material and limited in one mode at the present stage under the SALDI-MS platform, and the high coverage rate and the detection sensitivity of the lipid and the metabolites under the positive ion mode and the negative ion mode can not be considered simultaneously, the invention provides a mass spectrum chip combination for carrying out dual-mode detection on the lipid extract of a biological sample based on a mass spectrum method on the first aspect, it is characterized in that the mass spectrum chip combination comprises at least two silicon nano chips, the surfaces of different silicon nano chips have different silicon nano structures, the silicon nano structure is used as a sample application area without modification or after different modifications for respectively receiving the lipid extract of the biological sample and respectively scanning in a positive ion mode and a negative ion mode, and then screening the data in the same detection mode, and selecting the mass spectrum data with the widest peak number coverage range for statistical analysis.
The invention provides a mass spectrum chip for performing dual-mode detection on a biological sample lipid extract based on a mass spectrum method, wherein the mass spectrum chip is a silicon nano chip with the surface comprising at least two different silicon nano structures, and the silicon nano structures are not modified or are used as sample application regions after being modified differently and are respectively used for receiving the biological sample lipid extract and scanning in a positive ion mode and a negative ion mode respectively, so that data in the same detection mode are screened, and mass spectrum data with the widest peak number coverage range are selected for statistical analysis.
The third aspect of the present invention provides a method for lipid dual mode detection using the mass spectrometry chip combination of the first aspect of the present invention or the mass spectrometry chip of the second aspect of the present invention, comprising the steps of:
s1, respectively dripping the lipid extracts of the biological samples on different sample application areas, and drying;
s2, fixing the mass spectrum chip combination or the mass spectrum chip on a target holder;
s3, conveying the target supporting plate into a target chamber, scanning the sample application areas in a positive ion detection mode and a negative ion detection mode respectively to obtain mass spectrum data of each sample application area in the positive ion detection mode and the negative ion detection mode, and performing statistical analysis;
and S5, screening the data in the same detection mode, and selecting the mass spectrum data with the widest peak number coverage range for statistical analysis.
In the present invention, preferably, the silicon nanomaterial includes quantum dots, nanowires, nanotubes, and porous silicon.
Further preferably, the silicon nanomaterial is selected from one of a silicon nanowire, a metal-doped silicon nanowire, an organic-doped silicon nanowire, and a metal-and organic-co-doped silicon nanowire.
Preferably, the biological sample is selected from a cancer biological sample;
more preferably, the cancer biological sample is selected from a liver cancer sample, a stomach cancer sample, and an intestinal cancer sample.
Further preferably, the sample type is selected from one of whole blood, serum, plasma, interstitial fluid.
Preferably, the data screening method in the same detection mode is as follows: first, the raw mass spectra were pre-conditioned by baseline subtraction using FlexAnalysis 3.4 software (Bruker daltons). The intensity of the selected peak with S/N ≧ 5 is then normalized to the total ionic strength. The important peak information meeting both P <0.05 and VIP >1 is obtained by combining the results of orthogonal partial least squares discriminant analysis (OPLS-DA) in the double sample Student's t-test in MATLAB software and the SIMCA software (Umetrics AB, Umea, Sweden).
The fourth aspect of the present invention provides an application of the mass spectrometry chip set of the first aspect of the present invention or the mass spectrometry chip of the second aspect of the present invention in preparing a kit for performing dual-mode detection on a cancer biological sample based on a mass spectrometry method so as to identify the cancer.
The fifth aspect of the present invention provides a kit for dual-mode detection of lipid extract from cancer biological sample based on mass spectrometry, comprising the mass spectrometry chip set of the first aspect of the present invention or the mass spectrometry chip of the second aspect of the present invention.
Preferably, the mass spectrometry chip assembly or the mass spectrometry chip comprises a first spotting region and a second spotting region, the first spotting region consisting of unmodified silicon nanostructures for receiving the cancer biological sample lipid extract and obtaining its mass spectrometry detection data in negative ion mode; the second sample application region consists of a metal and polydopamine modified silicon nanostructure and is used for receiving the cancer biological sample lipid extract and obtaining mass spectrum detection data of the cancer biological sample lipid extract in a positive ion mode; the metal is selected from one of Au, Ag, Cu and Pt.
Preferably, the silicon nanomaterial comprises quantum dots, nanowires, nanotubes, porous silicon.
Further preferably, the silicon nanomaterial is selected from one of a silicon nanowire, a metal-doped silicon nanowire, an organic-doped silicon nanowire, and a metal-and organic-co-doped silicon nanowire.
In some embodiments of the invention, the mass spectrometry chip assembly is composed of a silicon nanowire chip and a silicon nanowire chip modified by metal and polydopamine together, in this case, the silicon nanostructure is a silicon nanowire array on the silicon nanowire chip, the silicon nanowire array on the silicon nanowire chip is not modified to form a first spotting region, and the silicon nanowire array modified by metal and polydopamine together on the silicon nanowire chip modified by metal and polydopamine together forms a second spotting region.
Preferably, the preparation method of the silicon nanowire chip modified by the metal, the metal and the polydopamine together is as follows: 1) preparing a silicon nanowire chip with a vertical nanowire array; 2) silanizing and modifying the silicon nanowire chip obtained in the step 1) by using a silanization reagent to obtain a silane-modified silicon nanowire chip; 3) performing metal modification on the silane-modified silicon nanowire chip obtained in the step 2) to obtain a metal-modified silicon nanowire chip; 4) and (3) further carrying out polydopamine modification on the metal-modified silicon nanowire chip obtained in the step 3), thus obtaining the metal and polydopamine jointly-modified silicon nanowire chip.
In some embodiments of the present invention, the specific process of step 3) is as follows: immersing the silicon nanowire chip into a metal nanoparticle colloidal solution, reacting for 15-180 min, washing with deionized water, and then using N2And (5) drying.
In some embodiments of the present invention, the specific process of step 4) is as follows: immersing the silicon nanowire chip into a Tris buffer solution containing dopamine, reacting for 15-55 min, washing with deionized water and using N2And (5) drying.
The sixth aspect of the present invention provides a method for bimodal detection of lipid extract from cancer biological sample by using the kit for bimodal detection of lipid in biological sample based on mass spectrometry method according to the fifth aspect of the present invention, comprising the following steps:
s1', dropping a cancer biosample lipid extract onto the first spotting region and the second spotting region, respectively, and performing a drying process;
s2', fixing the processed mass spectrometry chip combination or mass spectrometry chip with the first spotting region and the second spotting region on a backing plate;
s3', feeding the target supporting plate into the target chamber, and scanning only the second sample application area in the positive ion detection mode; and scanning only the first sample application area in a negative ion detection mode to obtain the lipid mass spectrum data. Further, the method further comprises the step of performing statistical analysis on the mass spectrum data, specifically as follows:
1) performing baseline subtraction pretreatment on mass spectrum data, and screening out peaks with the signal-to-noise ratio S/N being more than or equal to 5;
2) normalizing the intensity of the selected peak with the signal-to-noise ratio S/N of more than or equal to 5 to the total ion intensity;
3) screening out important peak information with P <0.05 and a variable projection importance value >1 according to double-sample Student's t-test and orthogonal partial least square discriminant analysis;
4) further screening peak information with high contribution by using a random forest packet;
5) and constructing an artificial neural network model containing a training set, a verification set and a test set based on the selected data, and attributing characteristic peak information to characteristic lipids.
In some embodiments of the invention, the characteristic lipids include Phosphatidylethanolamine (PE) (O-34:2), Phosphatidylethanolamine (PE) (O-36:3), Phosphatidylethanolamine (PE) (O-36:2), Phosphatidylethanolamine (PE) (O-38:5), Phosphatidylethanolamine (PE) (O-40:8), Phosphatidylinositol (PI) (34:1), Lysophosphatidylcholine (LPC) (20:3), Diglyceride (DG) (36:4), Cholesterol Ester (CE) (18:2), Cholesterol Ester (CE) (20:4), Sphingomyelin (SM) (d34:1), Phosphatidylcholine (PC) (36:4), Triacylglycerol (TG) (52:3), and Triacylglycerol (TG) (56: 6).
In some preferred embodiments of the present invention, the lipid extract of cancer sample in S1' is prepared by the following method: cancer samples were taken and added to a solution of methyl tert-butyl ether/methanol (MTBE/MeOH), vortexed for 10min, and H was added2And O, continuing to swirl for 10min, centrifuging for 10min, taking the supernatant, repeating twice, drying under a nitrogen flow, and suspending the precipitate in IPA to obtain the cancer sample lipid extract, and storing at-20 ℃.
In some preferred embodiments of the present invention, the detection conditions of S3' are as follows: the diameter of the circular laser spot is set to be 100 mu m, the relative laser pulse energy is respectively set to be 60% of the maximum energy, and the measurement mode is set to be a reflection mode; performing 500 times of laser irradiation on a single point and superposing for 4 times to obtain a generated spectrum; for the negative ion detection mode, the mass range is set to be 400-; for the positive ion detection mode, the mass range is set to 400 ═ m/z 1000.
Preferably, the cancer biological sample is selected from liver cancer sample, stomach cancer sample and intestinal cancer sample.
The invention has the advantages of
Compared with the prior art, the invention has the following beneficial effects:
aiming at the problems that the serum lipid is mostly analyzed by adopting a single nano material and limited in one mode at the present stage and the high coverage rate and detection sensitivity of the lipid and metabolites in the positive ion mode and the negative ion mode cannot be considered simultaneously, the biological sample lipid dual-mode detection kit disclosed by the invention is used for detection, so that the background interference in the lipid detection range can be reduced, the high coverage rate and detection sensitivity of the lipid and metabolites in the positive ion mode and the negative ion mode can be considered simultaneously, the number of detected effective peaks can be increased to nearly 479, and the effective peaks simultaneously contain neutral lipid and polar lipid.
The biological sample lipid dual-mode detection kit is simple and convenient to use, and can be used for diagnosing diseases or predicting the occurrence risk of the diseases.
Drawings
FIG. 1 shows a flow chart of SiNW-Au-PDA preparation in the embodiment of the invention.
FIG. 2 shows an array of vertical nanowires of SiNW prepared in an embodiment of the invention.
FIG. 3 shows top view (a), cross-sectional view (b) and 45 ° oblique view (c) of the SiNW-Au-PDA SEM image.
FIG. 4 shows the total number of peaks (S/N.gtoreq.5) measurable in the LDI-MS analysis of serum lipids for example 3, comparative example 1 and comparative example 2.
Fig. 5 shows the results of differentiating (a) the model of comparative example 1, (b) the model of comparative example 2, (c) the model of example 3 between training set HCC patients and healthy control OPLS-DA.
Detailed Description
Unless otherwise indicated, implied from the context, or customary in the art, all parts and percentages herein are by weight and the testing and characterization methods used are synchronized with the filing date of the present application. Where applicable, the contents of any patent, patent application, or publication referred to in this application are incorporated herein by reference in their entirety and their equivalent family patents are also incorporated by reference, especially as they disclose definitions relating to synthetic techniques, products and process designs, polymers, comonomers, initiators or catalysts, and the like, in the art. To the extent that a definition of a particular term disclosed in the prior art is inconsistent with any definitions provided herein, the definition of the term provided herein controls.
The numerical ranges in this application are approximations, and thus may include values outside of the ranges unless otherwise specified. A numerical range includes all numbers from the lower value to the upper value, in increments of 1 unit, provided that there is a separation of at least 2 units between any lower value and any higher value. For example, if a compositional, physical, or other property (e.g., molecular weight, melt index, etc.) is recited as 100 to 1000, it is intended that all individual values, e.g., 100, 101, 102, etc., and all subranges, e.g., 100 to 166, 155 to 170, 198 to 200, etc., are explicitly recited. For ranges containing a numerical value less than 1 or containing a fraction greater than 1 (e.g., 1.1, 1.5, etc.), then 1 unit is considered appropriate to be 0.0001, 0.001, 0.01, or 0.1. For ranges containing single digit numbers less than 10 (e.g., 1 to 5), 1 unit is typically considered 0.1. These are merely specific examples of what is intended to be expressed and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.
When used with respect to chemical compounds, the singular includes all isomeric forms and vice versa (e.g., "hexane" includes all isomers of hexane, individually or collectively) unless expressly specified otherwise. In addition, unless explicitly stated otherwise, the use of the terms "a", "an" or "the" are intended to include the plural forms thereof.
The terms "comprising," "including," "having," and derivatives thereof do not exclude the presence of any other component, step or procedure, and are not intended to exclude the presence of other elements, steps or procedures not expressly disclosed herein. To the extent that any doubt is eliminated, all compositions herein containing, including, or having the term "comprise" may contain any additional additive, adjuvant, or compound, unless expressly stated otherwise. Rather, the term "consisting essentially of … …" excludes any other components, steps or processes from the scope of any of the terms hereinafter recited, insofar as such terms are necessary for performance. The term "consisting of … …" does not include any components, steps or processes not specifically described or listed. Unless explicitly stated otherwise, the term "or" refers to the listed individual members or any combination thereof.
In order to make the technical problems, technical solutions and advantageous effects solved by the present invention more apparent, the present invention is further described in detail below with reference to the following embodiments.
Examples
The following examples are used herein to demonstrate preferred embodiments of the invention. It will be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function in the invention, and thus can be considered to constitute preferred modes for its practice. Those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit or scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs and the disclosures and references cited herein and the materials to which they refer are incorporated by reference.
Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.
The molecular biological experiments, which are not specifically described in the following examples, were performed according to the specific methods listed in the manual of molecular cloning, laboratory manual (fourth edition) (j. sambrook, m.r. green, 2017), or according to the kit and product instructions. Other experimental methods, unless otherwise specified, are conventional. The instruments used in the following examples are, unless otherwise specified, laboratory-standard instruments; the test materials used in the following examples were purchased from a conventional biochemical reagent store unless otherwise specified.
Example 1 preparation of silicon nanowire chip (SiNW-Au-PDA) jointly modified by gold nanoparticles and polydopamine
The implementation of the preparation of the SiNW-Au-PDA comprises 3 steps, as shown in FIG. 1, a silicon nanowire chip (SiNW) with a vertical nanowire array is firstly prepared, then a gold nanoparticle (AuNP) solution with uniform particle size is prepared, and then the AuNP solution and a Dopamine (DA) solution are respectively utilized to carry out modification, so that the SiNW-Au-PDA chip is finally obtained.
The detailed steps are as follows:
step one, preparing a SiNW chip with a vertical nanowire array
The invention utilizes a one-step metal-assisted chemical etching method to prepare a SiNW chip, and the preparation process comprises the following steps:
p-type single crystal silicon was cut into a square of 2X 2cm in size with a diamond blade, and the square was immersed in a solution containing 4.8M hydrofluoric acid (HF) and 0.02M silver nitrate (AgNO)3) And reacting and etching in the solution for 10 min. After etching, the substrate was washed three times with deionized water and then immersed in dilute nitric acid (HNO)3) Soaked for 1h to dissolve the silver (Ag) catalyst.
By this step, a SiNW chip with vertical nanowire arrays can be fabricated (as shown in fig. 2).
Step two, preparing gold nanoparticle (AuNP) colloidal solution with uniform particle size
The inventors utilized traditional sodium citrate (Na)3Cit) reduction method for preparing AuNP, the specific preparation process is as follows:
when 50 mL0.01% of tetrachloroauric acid (HAuCl)4) After the solution was boiled, 415. mu.L of 1% Na was added3And the Cit solution is boiled for 30min, and finally a uniform AuNP colloidal solution is formed.
Step three, preparing the SiNW-Au-PDA chip
(1) Reacting the SiNW chip subjected to oxygen plasma treatment with a toluene solution (v/v) containing 2% of 3-Aminopropyltrimethoxysilane (APTES) at room temperature for 15 min;
(2) after the reaction is finished, sequentially washing the reaction product by using methylbenzene and absolute ethyl alcohol, and stabilizing the reaction product at 60 ℃ for 60min to obtain SiNW-APTES;
(3) soaking the prepared SiNW-APTES in AuNP colloidal solution, maintaining for 150min, washing with deionized water, and washing with N2Drying to obtain a gold-modified SiNW chip (SiNW-Au);
(4) the SiNW-Au was further immersed in 50mM Tris buffer (pH 8.5) containing 0.1M Dopamine (DA) for 15min, washed with deionized water and N2Drying by blowing, and finally preparing the SiNW-Au-PDA chip, wherein an SEM picture is shown in figure 3.
Example 2 preparation of serum lipid extract
Mu.l of serum was added to 335. mu.l of MTBE/MeOH solution, vortexed for 10min, and 65ul of H was added2And O, continuously vortexing for 10min, centrifuging for 10min, finally extracting the supernatant, repeatedly extracting the supernatant twice, drying under nitrogen flow, and suspending the precipitate in IPA to obtain serum lipid extract, and storing at-20 ℃.
Example 3 application of silicon nanomaterial combination for lipid bimodal detection of biological samples based on mass spectrometry method in serum lipid extract mass spectrometry
S1, cutting the SiNW chip and the SiNW-Au-PDA chip prepared in the embodiment 1 into small chips with the size of 3 x 3mm, and sucking 2 mul of the serum lipid extract obtained in the embodiment 2 to respectively drip on the SiNW chip and the SiNW-Au-PDA chip;
s2, fixing the SiNW chip and the SiNW-Au-PDA chip on the same aluminum target plate matched with the matrix-assisted laser desorption ionization time-of-flight mass spectrometry target chamber by using carbon conductive adhesive;
s3, conveying the aluminum target plate into a target chamber, setting the diameter of a circular laser spot to be 100 μm, setting the relative laser pulse energy to be 60% of the maximum energy respectively, and setting the measurement mode to be a reflection mode; performing 500 times of laser irradiation on a single point and superposing for 4 times to obtain a generated spectrum; for the negative ion detection mode, the mass range is set to be 400-; for the positive ion detection mode, the mass range is set to be m/z 400-; detecting only the SiNW chip in a negative ion detection mode to obtain mass spectrum data; and screening the data in the same detection mode, and finally selecting the mass spectrum data with the widest peak number coverage range.
Comparative example 1
Comparative example 1 is different from example 3 in that comparative example 1 employs the SiNW chip of example 1 in both positive and negative ion detection modes.
Comparative example 2
Comparative example 2 is different from example 3 in that comparative example 2 employs the SiNW-Au-PDA chip of example 1 in both positive and negative ion detection modes.
The mass spectrum data obtained in example 3 and comparative examples 1-2 were analyzed
The data set consisted of the average data from three replicates per sample. First, the raw mass spectra were pre-conditioned by baseline subtraction using FlexAnalysis 3.4 software (Bruker daltons). The intensity of the selected peak with S/N ≧ 5 is then normalized to the total ionic strength. And combining the results of orthogonal partial least squares discriminant analysis (OPLS-DA) in a double sample Student's t-test in MATLAB software and SIMCA software (Umetrics AB, Umea, Sweden), and obtaining important peak information which simultaneously satisfies P <0.05 and variable projection importance Value (VIP) > 1. Then, the peak information is further screened by using a random forest packet (http:// cran.r-project. org/web/packages/random forest /) in the R software, and the peak information with high contribution is screened. Finally, a diagnostic model is constructed based on the selected data set.
An Artificial Neural Network (ANN) model with a multi-layered perceptual structure (number of hidden neurons 10) was built in the pattern recognition tool of MATLAB software. In the ANN model, the training set accounts for 70% of the total data set, with 15% of the data being used as validation set. An additional 15% of the data was used as a test set to evaluate the sensitivity and specificity of the prediction.
The model of example 3, the model of comparative example 1, and the model of comparative example 2 were constructed based on the above procedures, respectively.
First, we divided the overall cohort into a discovery cohort and a validation cohort according to the age and gender of the subjects, as shown in table 1.
TABLE 1 basic information of the study cohort
The expression format for age is median (range).
As shown in FIG. 4, the number of effective peaks (S/N ≧ 5) detected by the three detection methods of example 3, comparative example 1 and comparative example 2, it can be seen that example 3 can achieve the maximum detection of serum lipid coverage.
In order to ensure the reliability of statistical comparison, the statistical models constructed by different chip combinations all contain the same characteristic lipid number, as shown in tables 2-4.
TABLE 2 characteristic lipid peaks screened using the model of example 3
TABLE 3 characteristic lipid peaks screened using the model of comparative example 1
TABLE 4 characteristic lipid peaks screened using the model of comparative example 2
The results of differentiating the model of example 3, the model of comparative example 1 and the model of comparative example 2 between the training set HCC patients and the healthy control group OPLS-DA are shown in fig. 5, and it can be found that the differentiating ability of the model constructed by using a single chip is inferior to that of the model obtained by using the combined chip.
In addition, we also evaluated the diagnosis accuracy of 3 models constructed on the data set by the model of example 3, the model of comparative example 1 and the model of comparative example 2 using the artificial neural network, and the results are shown in table 5.
TABLE 5 sensitivity, specificity and AUC values of different diagnostic models for HCC patients differentiated from healthy controls
The above experimental results show that the high coverage serum lipid analysis has important significance for the construction of good diagnostic models.
All documents referred to herein are incorporated by reference into this application as if each were individually incorporated by reference. Furthermore, it should be understood that various changes and modifications of the present invention can be made by those skilled in the art after reading the above teachings of the present invention, and these equivalents also fall within the scope of the present invention as defined by the appended claims.
Claims (10)
1. The mass spectrum chip combination is characterized by comprising at least two silicon nano chips, wherein different silicon nano chip surfaces have different silicon nano structures, the silicon nano structures are not modified or are subjected to different modifications and then serve as sample application areas to be respectively used for receiving the biological sample lipid extracts and scanning in a positive ion mode and a negative ion mode respectively, then data in the same detection mode are screened, and mass spectrum data with the widest peak number coverage range are selected for statistical analysis.
2. The mass spectrum chip is characterized in that the surface of the mass spectrum chip comprises at least two silicon nano-structures, the silicon nano-structures are not modified or are modified differently and then are used as sample application areas for receiving the biological sample lipid extracts respectively and used for scanning in a positive ion mode and a negative ion mode respectively, then data in the same detection mode are screened, and mass spectrum data with the widest peak number coverage range are selected for statistical analysis.
3. A method of dual mode detection of lipids using the mass spectrometry chip assembly of claim 1 or the mass spectrometry chip of claim 2, comprising the steps of:
s1, respectively dripping the lipid extracts of the biological samples on different sample application areas, and drying;
s2, fixing the mass spectrum chip combination or the mass spectrum chip on a target holder;
s3, conveying the target supporting plate into a target chamber, scanning the sample application areas in a positive ion detection mode and a negative ion detection mode respectively to obtain mass spectrum data of each sample application area in the positive ion detection mode and the negative ion detection mode, and performing statistical analysis;
and S5, screening the data in the same detection mode, and selecting the mass spectrum data with the widest peak number coverage range for statistical analysis.
4. Use of the mass spectrometry chip set of claim 1 or the mass spectrometry chip set of claim 2 in the preparation of a kit for dual-mode detection of a cancer biological sample based on mass spectrometry to identify the cancer.
5. A kit for dual-mode detection of lipid extracts from cancer biological samples based on mass spectrometry, comprising the mass spectrometry chip assembly of claim 1 or the mass spectrometry chip of claim 2.
6. The kit for the dual-mode lipid detection of the biological sample based on the mass spectrometry method as claimed in claim 5, wherein the mass spectrometry chip assembly or the mass spectrometry chip comprises a first sample application region and a second sample application region, the first sample application region is composed of unmodified silicon nano-structures and is used for receiving the lipid extract of the cancer biological sample and obtaining the mass spectrometry detection data of the lipid extract in the negative ion mode; the second sample application region consists of a metal and polydopamine modified silicon nanostructure and is used for receiving the cancer biological sample lipid extract and obtaining mass spectrum detection data of the cancer biological sample lipid extract in a positive ion mode; the metal is selected from one of Au, Ag, Cu and Pt.
7. A method for the bimodal detection of lipid extracts from cancer biological samples by using the kit for the bimodal detection of lipids in biological samples based on mass spectrometry method as claimed in claim 6, characterized in that it comprises the following steps:
s1', dropping a cancer biosample lipid extract onto the first spotting region and the second spotting region, respectively, and performing a drying process;
s2', fixing the processed mass spectrometry chip combination or mass spectrometry chip with the first spotting region and the second spotting region on a backing plate;
s3', feeding the target supporting plate into the target chamber, and scanning only the second sample application area in the positive ion detection mode; and scanning only the first sample application area in a negative ion detection mode to obtain the lipid mass spectrum data.
8. The method of claim 7, wherein the lipid extract from the cancer biosample at S1' is obtained by: adding methyl tert-butyl ether/methanol solution into the cancer biological sample, vortexing, collecting supernatant, repeating twice, drying under nitrogen flow, and suspending the precipitate in isopropanol to obtain cancer biological sample lipid extract.
9. The method of claim 7, further comprising the step of performing statistical analysis on the mass spectral data, in particular as follows:
1) performing baseline subtraction pretreatment on mass spectrum data, and screening out peaks with the signal-to-noise ratio S/N being more than or equal to 5;
2) normalizing the intensity of the selected peak with the signal-to-noise ratio S/N of more than or equal to 5 to the total ion intensity;
3) screening out important peak information with P <0.05 and a variable projection importance value >1 according to double-sample Student's t-test and orthogonal partial least square discriminant analysis;
4) further screening peak information with high contribution by using a random forest packet;
5) and constructing an artificial neural network model containing a training set, a verification set and a test set based on the selected data, judging the accuracy of the model, and attributing characteristic peak information to characteristic lipid.
10. The method of claim 9, wherein the characteristic lipid comprises Phosphatidylethanolamine (PE) (O-34:2), Phosphatidylethanolamine (PE) (O-36:3), Phosphatidylethanolamine (PE) (O-36:2), Phosphatidylethanolamine (PE) (O-38:5), Phosphatidylethanolamine (PE) (O-40:8), Phosphatidylinositol (PI) (34:1), Lysophosphatidylcholine (LPC) (20:3), Diglyceride (DG) (36:4), Cholesterol Ester (CE) (18:2), Cholesterol Ester (CE) (20:4), Sphingomyelin (SM) (d34:1), Phosphatidylcholine (PC) (36:4), Triacylglycerol (TG) (52:3), and Triacylglycerol (TG) (56: 6).
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