CN110887893A - MALDI-MS-based method for rapidly identifying fritillaria species - Google Patents
MALDI-MS-based method for rapidly identifying fritillaria species Download PDFInfo
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Abstract
The invention provides a method for rapidly identifying fritillaria species based on MALDI-MS, which comprises the following steps: extracting Bulbus Fritillariae Cirrhosae corm powder with extraction solvent, centrifuging, and collecting supernatant; spotting the supernatant on a target plate, drying, performing matrix spotting, and performing MALDI-MS analysis and detection; analyzing MALDI data by multivariate statistical analysis method, specifically establishing Bulbus Fritillariae Cirrhosae species identification model based on Bulbus Fritillariae Cirrhosae metabolite by PLS-DA method, and drawing tree-like cluster diagram by hierarchical clustering analysis method. The invention successfully develops an effective fritillaria species differentiation and identification method, the extraction, detection and method developed by the invention is very effective for researching the differentiation and identification of herbal plants, and the analysis and detection time is greatly shortened because no complex pretreatment process is needed.
Description
Technical Field
The invention belongs to the technical field of traditional Chinese medicine analysis, and particularly relates to a method for rapidly identifying fritillaria species based on MALDI-MS.
Background
The information in this background section is only for enhancement of understanding of the general background of the invention and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.
Fritillary bulb is one of the largest liliaceae species and is widely distributed worldwide. The bulb of fritillaria has been used as food and folk medicine in many asian countries. Among the traditional Chinese medicines, the bulb of fritillaria has been used as an antitussive and anti-asthma medicine for over 2000 years. According to the regulation of the Chinese pharmacopoeia (2015 edition), 11 species are officially recorded as plant sources of fritillary, and the species are classified into five types including fritillary, fritillary pallidiflorum, thunberg fritillary, unibract fritillary bulb and unibract fritillary bulb.
The main active component of fritillaria is steroid alkaloid, and the types of fritillaria alkaloid are different due to different genotypes, growth conditions and environments. Various secondary metabolites of fritillaria species lead to different pharmacological effects. However, due to the growing environment of fritillaria, commercial products, especially those from wild plants under extreme conditions, are difficult to obtain, resulting in fritillaria being expensive and prone to adulteration. Therefore, before pharmacological research and clinical application of fritillaria, species identification and identification of fritillaria are necessary.
Traditional methods for identifying and characterizing fritillaria include microscopic morphological observation, thin layer chromatography and DNA molecular techniques. However, morphological observation requires professional knowledge in pharmacology, and thin layer chromatography uses only a few standard compounds as differentiation references, and cannot consider other active ingredients. Although DNA technology can identify genotypes of different species, it requires cumbersome experimental procedures and does not provide direct information about pharmacologically active alkaloids. Therefore, there is an urgent need to develop a new method for differentiation and identification of different fritillary species.
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a simple but powerful mass spectrometry technique that can be used for rapid MS analysis of various types of metabolites without prior chromatographic separation. MALDI-MS analysis techniques are tolerant to impurities, making them suitable methods for analyzing metabolites in complex samples of herbaceous plants. The inventors have found that at present, multivariate statistical analysis of MALDI-MS and MS metabolites has not been applied to the identification of fritillary species.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention aims to provide a method for quickly identifying fritillaria species based on MALDI-MS. The invention firstly uses the MALDI-MS analysis method for directly detecting the fritillaria sample, does not need complex pretreatment, and has less sample consumption and high analysis speed. The multivariate variable statistical analysis method of the MS metabolites developed by the invention can sensitively and rapidly realize the identification of the fritillaria species, thereby having good practical application value.
In order to achieve the purpose, the technical scheme of the invention is as follows:
in a first aspect of the invention, there is provided the use of MALDI-MS analysis and/or multivariate statistical analysis of MS metabolites for identifying fritillary species.
In a second aspect of the present invention, a method for rapidly identifying fritillaria species is provided, which comprises the following steps:
extracting Bulbus Fritillariae Cirrhosae corm powder with extraction solvent, centrifuging, and collecting supernatant;
spotting the supernatant on a target plate, drying, performing matrix spotting, and performing MALDI-MS analysis and detection;
the MALDI data was analyzed using multivariate statistical analysis. Specifically, a fritillaria species identification model based on fritillaria metabolites is established by adopting a PLS-DA (partial least squares data analysis) method. And drawing a dendriform clustering chart by adopting a hierarchical clustering analysis method, thereby successfully developing an effective fritillaria species differentiation and identification method.
The invention has the beneficial technical effects that:
the method for detecting the fritillaria sample to be detected by the MALDI-MS method is rapid, simple and convenient. The statistical analysis is carried out on the detection result of the MALDI-MS by adopting a multivariate variable statistical analysis method, so that the identification of the species of the fritillaria can be accurately realized, thereby identifying counterfeit or adulterated fritillaria, helping to identify whether the commercially available fritillaria is fake or genuine and is good in quality, and whether the fritillaria is the type of the fritillaria marked by the label, and avoiding the deception of consumers by false information, so that the method has good value of practical application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a MALDI-MS diagram of fritillary bulb with different extraction solvents in example 1 of the present invention.
FIG. 2 is a MALDI-MS diagram of fritillary bulb at different extraction times in example 1 of the present invention.
FIG. 3 is a MALDI-MS diagram of fritillary bulb with different detection matrixes in example 1 of the present invention.
FIG. 4 is a diagram showing the detection of PLS-DA by the fritillaria bulb based on MALDI-MS in example 2 of the present invention.
FIG. 5 is a dendrogram of the MALDI-MS based detection of fritillaria in example 2 of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. 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 application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The present invention is further illustrated by reference to specific examples, which are intended to be illustrative only and not limiting. If the experimental conditions not specified in the examples are specified, they are generally according to the conventional conditions, or according to the conditions recommended by the sales companies; the present invention is not particularly limited, and may be commercially available.
As described in the background, at present, multivariate statistical analysis of MALDI-MS and MS metabolites has not been applied to the identification of fritillary species.
The method detects the fritillaria sample to be detected by a MALDI-MS method for the first time; in view of the above, in a typical embodiment of the present invention, an application of the MALDI-MS analysis method and/or the multivariate statistical analysis method of MS metabolites in identifying fritillaria species is provided.
In another embodiment of the present invention, a method for rapidly identifying fritillaria species is provided, which comprises:
extracting Bulbus Fritillariae Cirrhosae corm powder with extraction solvent, centrifuging, and collecting supernatant;
spotting the supernatant on a target plate, drying, performing matrix spotting, and performing MALDI-MS analysis and detection;
analyzing the MALDI data by adopting a multivariate variable statistical analysis method; specifically, a fritillaria species identification model based on fritillaria metabolites is established by a PLS-DA (partial least square data analysis) method, and a dendriform cluster map is drawn by a hierarchical clustering analysis method.
In another embodiment of the present invention, the preparation method of the fritillaria bulb powder comprises: grinding dried bulb of Bulbus Fritillariae Cirrhosae to powder.
In another specific embodiment of the invention, the extraction is ultrasonic extraction, and the ultrasonic extraction time is 1-60 minutes. The invention is verified by experiments that the extraction time of 1 minute is enough to extract the fritillaria sample, so that the ultrasonic treatment is preferably used for 1 minute.
In still another embodiment of the present invention, the extraction solvent includes, but is not limited to, acetonitrile, methanol, acetone, chloroform and water, more preferably methanol and acetonitrile, and still more preferably 50% acetonitrile aqueous solution. The experiment proves that the extraction effect of the 50 percent acetonitrile water solution is the best.
In one embodiment of the present invention, the detection matrix includes, but is not limited to, α -cyano-4-hydroxycinnamic acid (CHCA), Sinapic Acid (SA) matrix, and 2, 5-dihydroxybenzoic acid matrix (DHB), and more preferably CHCA.
The concentration of the detection matrix is 1-20 mg/mL, preferably 10mg/mL, and experiments prove that the peak effect of the fritillaria extracting solution is better when 10mg/mLCHCA is used as the matrix.
In one embodiment of the present invention, the volume of the sample of the supernatant and the matrix dropped on the target plate is 0.5 to 2 microliters, preferably 1 milliliter.
In another embodiment of the present invention, the mass spectrometry conditions of MALDI-MS are: the acquisition interval is 100-1000 times, the acquisition mode is a reflection mode, and the scanning frequency is 200 times of accumulation.
In another embodiment of the invention, the multivariate statistical analysis method comprises the steps of establishing a fritillaria species identification model based on fritillaria metabolites by using a PLS-DA (partial laboratory data analysis) method; and drawing a dendriform clustering chart by adopting a hierarchical clustering analysis method, thereby developing an effective fritillaria species differentiation and identification method.
In another embodiment of the present invention, the detection method further comprises processing the fritillaria metabolite data based on the alignment and zero padding, and plotting PLS-DA analysis graphs and arborescence cluster graphs of different species of fritillaria.
The invention is further illustrated by the following examples, which are not to be construed as limiting the invention thereto. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention.
Example 1
The extraction and MALDI-MS analysis method of metabolite in fritillaria comprises the following steps:
(1) instruments and reagents
The mass spectrometer used in the embodiment of the invention is a Bruker rapifleX tissue system; the balance used was a Mettler Toledo XS 105 electronic balance.
The fritillary bulb samples used (fritillary bulb, fritillary pallium, fritillary thunberg, fritillary thunbergii, fritillary bulb) were provided by professor wang dawn, a pharmacological expert at Sichuan university, the reagents used were chromatographically pure acetonitrile, methanol, acetone and chloroform from Merck, Germany, and the substrates used were 2, 5-dihydroxybenzoic acid (DHB), Sinapinic Acid (SA) substrate and α -cyano-4-hydroxycinnamic acid (CHCA) substrate from Sigma.
(2) Sample extraction
The dried bulb of fritillary bulb was ground to powder by a mortar grinder. Weighing a proper amount of powder, performing ultrasonic extraction by using an extraction solvent, centrifuging, and taking supernatant for further MALDI-MS experiment.
(3) Conditions of Mass Spectrometry
Bruker rapifleX tissue system mass spectrometer: a positive ion scanning mode; scanning range: m/z 100-1000; laser wavelength: 355 nm; adopting a reflection mode; sampling rate: 2.5GS s-1; acceleration potential: 20kV, vacuum pressure: 3-5x10-7 mbar; accumulating frequency: 200.
all MS mass spectra were calibrated with malachite molecules (C23H25N2+, m/z 329.2102).
(4) Results
4.1 selection of the type of extraction solvent for Fritillaria
The extraction effect of 7 solvents of acetonitrile (50% and 100%), methanol (50% and 100%), deionized water (100%), acetone (100%) and chloroform (100%) on fritillaria samples was examined. Experiments show that the extraction effect of the fritillaria sample is better by 50% methanol and 50% acetonitrile. In comprehensive consideration, the invention selects 50 percent of acetonitrile as a preparation solvent.
4.2 selection of extraction time of Fritillaria
The effect of different ultrasound extraction times (1,2,5,10,30 and 60 minutes) on the extraction of fritillaria samples was examined. It was found experimentally that an extraction time of 1 minute was sufficient to extract a sample of fritillaria. Thus, the present invention selects an ultrasound extraction time of 1 minute.
4.3 selection of fritillary bulb sample test matrix
The influence of three matrixes, namely 10mg/mL CHCA, 10mg/mL SA and 10mg/mL DHB, on the detection of the fritillaria extracting solution is examined. Experiments show that the peak effect of the fritillaria extracting solution is better when CHCA is used as a matrix. Thus, the present invention selects CHCA as the test matrix for fritillaria extract.
4.4 optimization of MALDI-MS Mass Spectrometry Scan frequency
The influence of MALDI-MS mass spectrum scanning frequency of 50, 100 and 200 on the signal of fritillaria extracting solution is examined. Experiments show that the signal intensity of fritillaria extracting solution is enhanced along with the increase of the scanning frequency of the mass spectrum, and the scanning frequency of the mass spectrum of 200 is selected comprehensively.
Example 2
The multivariate statistical analysis method of fritillary metabolite comprises the following steps:
(1) MALDI-MS data deconvolution and derivation
The collected MALDI MS data was first processed by commercial software flexAnalysis 4.0(Bruker daltons, germany). The SNAP algorithm was used for MS peak deconvolution. And exporting the deconvolved MS data with the signal-to-noise ratio of more than 6 into an excel file for further processing.
(2) MALDI-MS data peak alignment and zero padding
MS data was aligned and zero padded by an internally written MATLAB program 2016b (MATLAB, usa). First, the program subtracts the background signal for each MS data set and then converts the MS data into an N × 2 matrix, where N is the number of variables. The data matrices for all species are aligned by m/z variables. For MS signals not detected at some aligned m/z, the intensity is assigned to zero. Thus, the program creates an integrated data set containing the same number of variables for all the species investigated.
(3) Multivariate data analysis
Data were imported into SIMICA-P software version 11.0 (umemetrics AB,
sweden) is biased to a minimum of twoData analysis (PLS-DA) was multiplied to establish a fritillaria species discrimination model based on metabolites of fritillaria species.
Data import was hierarchical clustering by R software 3.6.1(R basis for statistical calculations, usa). R processing was performed on the data matrix obtained by metabolomics analysis. And calculating Euclidean distance, and generating a tree-shaped clustering graph by using an average clustering algorithm.
(4) Results
4.1 PLS-DA analysis chart of metabolites of fritillaria of different species
PLS-DA analysis is used to find the maximum degree of sample separation to achieve discrimination between groups of samples. We first applied PLS-DA to build classification models for different fritillary species. To validate the model, permutation tests were performed to calculate goodness-of-fit (R2) and predictive power of the model (Q2). The model has good predictability when Q2 is close to 1. Q2 for the PLS-DA model was calculated to be 0.937. From the 3D analysis plot, it was found that all five species of fritillaria were isolated, which indicates that our MALDI-MS method can be used to rapidly distinguish different species of fritillaria.
4.2 dendrimer graphs of metabolites of fritillaria of different species
Taxonomically, it is a reasonable assumption that plants of genetic origin and close geographical location have similar metabolomic characteristics. In other words, the similarity of metabolomic features should provide a hypothetical insight into the relationship between species. And calculating the Euclidean distance according to the NxK configuration matrix, and then generating a tree-shaped cluster map in the R environment through an average clustering algorithm. It can be seen that the close relationship between Chuan-Bei mu and the dark purple Bei mu exists, while the outlier relationship between Wabu Bei mu and the above two groups exists, which is consistent with the genetic analysis of Bei mu by professor Wang Shu. This shows that our metabonomics approach can provide valuable hints for the taxonomy of Chinese herbal medicine.
5. Conclusion
The invention adopts MALDI-MS technology, successfully establishes five rapid differentiation methods of representative fritillaria species for the first time. The whole extraction process is simple and convenient to operate, and the test process can be completed within a few minutes. The established PLS-DA analysis and hierarchical clustering analysis method can accurately realize the differentiation of different fritillary species, and the extraction, detection and method developed by the invention is very effective for researching the differentiation and identification of herbal plants.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
- Use of MALDI-MS analysis and/or multivariate statistical analysis of MS metabolites for identification of fritillary species.
- 2. A method for rapidly identifying fritillaria species is characterized by comprising the following steps:extracting Bulbus Fritillariae Cirrhosae corm powder with extraction solvent, centrifuging, and collecting supernatant;spotting the supernatant on a target plate, drying, performing matrix spotting, and performing MALDI-MS analysis and detection;MALDI data were analyzed using multivariate statistical analysis.
- 3. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein the fritillaria bulb powder is specifically prepared by a method comprising: grinding dried bulb of Bulbus Fritillariae Cirrhosae to powder.
- 4. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein the extraction solvent comprises acetonitrile, methanol, acetone, chloroform and water, preferably methanol and acetonitrile, more preferably 50% acetonitrile in water.
- 5. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein the ultrasonic extraction time is 1-60 minutes.
- 6. The method for rapid identification of fritillary species as claimed in claim 2 wherein the detection matrix comprises CHCA, SA and DHB, preferably CHCA; or the like, or, alternatively,the concentration of the detection matrix is 1-20 mg/mL, preferably 10 mg/mL.
- 7. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein the sample volume of the supernatant and the matrix dropped on the target plate is 0.5-2 ml, preferably 1 μ l.
- 8. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein MALDI-MS is performed under mass spectrometry conditions of: the acquisition interval is 100-1000 times, the acquisition mode is a reflection mode, and the scanning frequency is 200 times of accumulation.
- 9. The method for rapidly identifying fritillaria species as claimed in claim 2, wherein the multivariate statistical analysis method comprises establishing a fritillaria species identification model based on fritillaria metabolites by using a PLS-DA method; and drawing a tree cluster map by adopting a hierarchical clustering analysis method.
- 10. The method of claim 2, further comprising processing fritillary metabolite data based on the alignment and zero padding, plotting PLS-DA analysis plots and arborescence clustering plots for different species of fritillary.
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