Method for visualizing spatial and temporal distribution of corn kernel metabolites by using mass spectrum imaging technology

Abstract

The invention relates to an experimental method for qualitatively and quantitatively detecting metabolites in a corn kernel filling period by using a mass spectrum imaging technology. The experimental method comprises the following steps: selecting a proper corn kernel sample, preparing a frozen slice, detecting by using a mass spectrometer, and analyzing and visualizing the obtained data by using a bioinformatics technology. The method has the characteristics that the use of chemical or radioactive labeling is avoided, the pretreatment process of a sample is simplified, the mass spectrum imaging technology is utilized to directly perform quantitative and qualitative analysis on the metabolite in the corn tissue at high flux, the spatial distribution information integrity of the metabolite is maintained, and meanwhile, the method is organically combined with bioinformatics analysis, so that the method has a wide application prospect. The spatial distribution characteristics of main stored metabolites in the corn kernels are disclosed, and abundant metabolome big data resources are provided for improving the nutritional quality of the corn kernels. The method is also suitable for other similar plant tissues.

Description

Technical Field

[0001] The present invention relates to the field of plant microscopy technology and plant metabolite data mining, and specifically to a method for mining the spatiotemporal dynamic changes of metabolites during the grain filling period of cultivated corn using mass spectrometry imaging technology. While quantifying and qualitatively analyzing metabolites, the spatial location information of the metabolites is retained, thus facilitating the exploration of the spatial distribution of three major storage metabolites. Background Art

[0002] The research goal of metabolomics is to conduct qualitative and quantitative analysis of all metabolites in biological systems as much as possible. It is a powerful analytical tool for studying metabolite spectra, metabolic phenotypes, metabolic changes, metabolic pathways and discovering biomarkers. Chromatography-mass spectrometry combines the efficient separation capability of chromatography with the powerful analytical function of mass spectrometry. It has the advantages of high sensitivity, good selectivity, wide dynamic range and rich information. It has become the most commonly used analytical technology in metabolomics research. It can obtain information on thousands of metabolites in a single analysis and occupies an important position in the metabolomics analysis of biological samples such as plants and animals. However, for the analysis of metabolites in biological tissues or organs with complex structures and high heterogeneity, the chromatography-mass spectrometry analysis method has limitations because the pre-treatment processes such as tissue homogenization, metabolite extraction, purification and enrichment required in the analysis process lose the spatial distribution information of metabolites in the tissue.

[0003] Mass spectrometry imaging (MSI) is a new type of molecular imaging technology that can directly obtain a large amount of known or unknown endogenous and exogenous metabolites and other molecular structure, content and spatial distribution information from biological tissues. Compared with other imaging methods (such as fluorescence imaging, radiolabeled imaging, etc.), this technology does not require chemical or radiolabeling, does not require complex sample pretreatment, and has the outstanding advantages of high specificity, high throughput and spatial information retention. Mass spectrometry imaging technology can achieve qualitative, quantitative and localization analysis of thousands of metabolites in biological tissues. Combined with bioinformatics analysis, differential metabolites can be found in situ from biological tissues and their biological functions can be identified. AFADESI-MSI is a highly sensitive, high-coverage, wide dynamic range and strong specificity imaging analysis technology for metabolites in tissues. The sensitivity can reach the pg level, and the dynamic range of metabolite content spans 3 orders of magnitude. It realizes the precise characterization of tissue micro-regions and the accurate identification of metabolites with similar mass numbers. It can perform metabolite spatial imaging analysis on samples with a tissue size of 1mm×1mm to 15cm×10cm. This technology solves technical problems such as the difficulty in detecting low-content metabolites in biological tissues, the small number of covered species, and the poor imaging quality. It can realize the simultaneous imaging analysis of about 1,500 metabolites in biological tissues, including choline, polyamines, amino acids, carnitines, nucleosides, nucleotides, organic acids, carbohydrates, cholesterol, bile acids, and lipids, and accurately characterize and identify the distribution characteristics of functional metabolites in tissue subregions. AFADESI-MSI technology has been widely used in the research of animal disease diagnosis, such as the study of the correlation between changes in the content of lipid metabolites and tumor diseases, drug targets and biomarkers, and the diagnosis and treatment of brain and nerve diseases. It has also been applied to some model plants, but its application in other crops is relatively small.

[0004] Corn is an important food crop in my country, and its kernel is its most important tissue organ. It is widely used as feed and industrial raw materials. The main reason is that it is rich in three major nutrients (carbohydrates, proteins and fats). Among them, oils are mainly stored in the embryo, while proteins and starch are mainly stored in the endosperm in the form of protein bodies and starch bodies. About 10 days after pollination, the kernel begins to enter the storage material synthesis and filling period. In this process, the photosynthetic products produced by the leaves through optical effects are transferred to the corn kernels through maternal tissues for the synthesis of storage materials. At this time, the three major nutrients in the endosperm begin to accumulate rapidly. However, its specific synthesis pathway is an extremely complex regulatory process, and there are still few reports on its related regulatory mechanisms. The use of AFADESI-MSI technology helps to visualize the spatial distribution of the three major nutritional metabolites in corn kernels, providing strong basic data for revealing their specific synthesis regulatory mechanisms, and thus providing new ideas for corn kernel improvement. Summary of the invention

[0005] The purpose of the present invention is to use AFADESI-MSI technology to visualize the spatiotemporal dynamic changes of metabolites in the grain filling period of cultivated corn in response to the above-mentioned scientific problems, so as to provide valuable candidate metabolites for improving corn grain nutrition and future breeding.

[0006] The present invention provides a method for qualitatively and quantitatively determining the spatiotemporal dynamic changes of corn kernel metabolites, comprising the following steps:

[0007] 1. Select appropriate corn kernel samples: Pick three well-developed ears of W64A inbred line 12 days after pollination and 18 days after pollination.

[0008] 2. Preparation of frozen sections: Select one fully developed kernel from each ear and place it in an embedding box, add embedding gel, and place it on dry ice for slow freezing. Add a little embedding gel to the tissue tray to fix the sample, place the fixed tissue sample on the sample head of the slicer, and cut frozen tissue sections of a set thickness. Dry the slices to be machined at -20°C for 1 hour, and then dry them at room temperature for 2 hours. Select adjacent slices to be machined for hematoxylin-eosin (H&E) staining.

[0009] 3. Mass spectrometry imaging scanning analysis: It was carried out in series using an AFADESI-MSI platform (Beijing Victor Technology Co., LTD, Beijing, China) and a Q-Orbitrap mass spectrometer (Q Exactive, Thermo Scientific, USA).

[0010] 4. Bioinformatics analysis: a series of analyses including mass spectrometry data processing, sample segmentation, database search, clustering and dimension reduction are performed. Since different substances have different preferences for positive and negative ions, two adjacent slices will be used for each sample in the present invention, and the positive and negative ion modes will be used for on-machine detection to ensure that as many substances as possible are detected. In addition, the cross-section size of a corn seed is about 20 mm2, and there is no special microstructure inside it, so a spatial resolution of 50 μm is selected to maximize the accurate identification and clear imaging of the substances of interest.

[0011] The beneficial effects of the present invention are:

[0012] The present invention develops a high-throughput qualitative and quantitative method for the spatiotemporal dynamic changes of cultivated corn kernel metabolites, which provides new tools and methods for in-depth studies of cell functions and metabolic pathways. Its advantages include: 1) A high-throughput, high-resolution method for analyzing corn kernel metabolites has been established, which can directly detect metabolites in the three major nutrient metabolic pathways in frozen sections, thereby achieving qualitative and quantitative analysis of corn kernel metabolites. 2) Through this method, the spatial distribution and expression abundance of thousands of metabolites in corn kernels can be visualized, thereby gaining an in-depth understanding of cell functions and metabolic pathways. 3) Metabolome data are used to perform precise cell clustering analysis on kernels, and no longer rely on technicians with high professional skills to perform freehand or laser microdissection, which is a difficult, time-consuming and inaccurate process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 These are the white slices of the W64A-12 sample (2 samples, 3 replicates, 2 ions, 12 slices in total). a is sample W64A-12, W64A-12 represents 12 days after pollination, b is sample W64A-18, W64A-18 represents 18 days after pollination.

[0014] Figure 2 It is the composition ratio of the spray solvent in the positive and negative ion scanning mode.

[0015] Figure 3 is a mass spectrum. a is the positive ion mode of W64A-12, b is the positive ion mode of W64A-18. The horizontal axis is the mass-to-charge ratio, and the vertical axis is the expression intensity of the corresponding mass-to-charge ratio.

[0016] Figure 4 is the SSCC cluster diagram. a is the W64A-12 cluster diagram, which is clustered into 11 regions, namely 1, 6-hard endosperm, 2, -aleurone layer, 3-chalaza, 4-seed coat, 5, 10-subaleurone layer, 7-embryo, 8-floury endosperm, 9-basal endosperm transport layer, 11-hard endosperm. b is the W64A-18 cluster diagram, which is also clustered into 11 regions, namely 1-embryo, 2-floury endosperm, 3, 5-subaleurone layer, 4-basal endosperm transport layer, 6-chalaza, 7-hard endosperm, 8-seed coat, 9-aleurone layer, 10, 11-tissue blank or noise area. Figure 5 This is an example of a visualized metabolite profile of corn kernels. a is an image of fructose, showing broad-spectrum expression, and b is an image of phosphoglyceride, showing embryo and aleurone-specific expression.

[0017] Figure 6It is the expression level of intermediate metabolites (5 examples each) in the protein, starch and lipid metabolic pathways of corn kernels in each partition (mz is the mass-to-charge ratio, Metabolites is the metabolite name, and max is the maximum expression level). PC-chalaza, SE-floury endosperm, PE-seed coat, AL-aleurone layer, BETL-basal endosperm transport layer, VE-hard endosperm, CZ-conduction zone, EM-embryo. DETAILED DESCRIPTION

[0018] 1. Select appropriate corn kernel samples

[0019] Corn grain filling starts after pollination, when storage substances begin to synthesize. However, in order to prevent the delay of grain development caused by pollination temperature and weather from affecting the experiment, grains 12 days after pollination were selected as materials for the beginning of the grain filling period, and then grains 18 days after pollination, 6 days away from this period, were selected as materials for the middle of the grain filling period. In order to ensure the accuracy of the experimental data, three ears with better development were selected in each period, and three replicates were made.

[0020] 2. Cryosection Preparation

[0021] Select one fully developed kernel from each ear and place it in an embedding box, add embedding gel, and place it on dry ice for slow freezing. Add a little embedding gel to the tissue tray to fix the sample, place the fixed tissue sample on the sample head of the slicer, and cut frozen tissue sections of a set thickness. Select two consecutive sections for each sample, dry them at -20°C for 1 hour before putting them on the machine, and then dry them at room temperature for 2 hours. Select adjacent sections to the machine section for hematoxylin-eosin (H&E) staining.

[0022] 3. Mass spectrometry imaging scanning analysis

[0023] (1) The dried slide was scanned, wherein in the negative ion mode, the spray solvent was acetonitrile (ACN) / H2O (8:2), and in the positive ion mode, the spray solvent was ACN / H2O (8:2, 0.1% formic acid).

[0024] (2) Imaging data acquisition adopts a line-by-line scanning method. After each line scan is completed, in order to maintain synchronization with the mass spectrometry data acquisition, a certain delay time (Dt, s) is usually required to be set, and then the sample stage quickly returns to the scanning starting point of the same line at a speed of 10mm / s to avoid contamination and damage to the undetected lines. Then, after stepping along the Y-axis for the set displacement distance, the next line scan is started according to the above steps, and the cycle is repeated until all sample area scans are completed to ensure the accuracy and reliability of the mass spectrometry imaging results. (The experimental platform parameters in the present invention are set as follows: the X-axis scanning speed is 0.2mm / s, the Y-axis scanning speed is 0.1mm, the step between scanning lines is 50μm, and the delay time for each line is 7s).

[0025] 4. Bioinformatics Analysis

[0026] (1) The raw data (.raw format) was converted into .imzML format files using imzMLConverter processing software and imported into the Cardinal software package for background subtraction, peak alignment, and peak screening. The m / z intensity of each pixel was then averaged to obtain the mass spectrum of each sample to show the expression intensity of the corresponding mass-to-charge.

[0027] (2) Metabolites in high-resolution mass spectrometry were annotated using the LuMet-Space 1.0 database and the PySM and mass2adduct annotation frameworks. Ultimately, 1,081 metabolites were annotated in cultivated maize kernels.

[0028] (3) The spatial shrunken centroids clustering (SSCC) algorithm was used to cluster the samples according to the different ion abundances in each pixel. Finally, the samples from the two periods were clustered into 11 zones each. (4) Unsupervised principal component analysis (PCA) was performed to observe the overall distribution of the samples and the stability of the entire analysis process. It was found that the three repeated samples in each period showed similar characteristics, indicating high consistency and stability. In contrast, the samples between different periods showed significant differences, indicating the significance of the changes between periods. Overall, these findings indicate that the data quality is good and provide a reliable basis for further in-depth research.

[0029] (5) Perform imaging analysis on all mass-charges to obtain their distribution information in the tissue slices, and finally select mz according to the needs to draw a smoothed image.

Claims

1. A method for visualizing the spatiotemporal distribution of metabolites in maize grain filling period using mass spectrometry imaging technology, characterized in that: The following steps are involved: 1) Select appropriate corn kernel samples: Pick ears of W64A inbred line that are in the filling stage and well developed. 2) Preparation of frozen sections: Select the well-developed and well-shaped kernels in the middle of the ear and place them in embedding boxes, add embedding glue, and place them on dry ice for slow freezing. Add a little embedding glue to the tissue tray to fix the sample, place the fixed tissue sample on the sample head of the slicer, and cut frozen tissue sections of a set thickness. Dry the slices to be machined at -20°C for 1 hour, and then dry them at room temperature for 2 hours. Select adjacent slices to be machined for hematoxylin-eosin staining. 3) Mass spectrometry imaging scanning analysis: It was carried out in series using an AFADESI-MSI platform (Beijing Victor Technology Co., LTD, Beijing, China) and a Q-Orbitrap mass spectrometer (Q Exactive, Thermo Scientific, USA). 4) Bioinformatics analysis: Perform a series of analyses including mass spectrometry data processing, sample segmentation, library search and qualitative analysis, clustering and dimensionality reduction, etc.

2. The method according to claim 1, characterized in that In step (1), in order to prevent the error of delayed grain development caused by pollination temperature and weather from affecting the experiment, grains 12 days after pollination were selected as materials for the beginning of the grain filling period, and then grains 18 days after pollination, 6 days away from this period, were selected as materials for the middle of the grain filling period. At this time, the slice quality is high and the metabolites are rich, ensuring that the mass spectrometry imaging technology can detect the metabolites that have reached abundance.

3. The method according to claim 1, characterized in that In step (1), in order to ensure the accuracy of the experimental data, three ears with better development were selected in each period, and three replicates were made to detect and control the experimental error.

4. The method according to claim 1, characterized in that: In step (2), two consecutive slices are selected from each sample for drying, and finally a total of 12 slices are dried.

5. The method according to claim 1, characterized in that: In step (2), the sections are first fixed for 30 minutes using a methanol solution precooled at -20°C for 30 minutes and then stained to prevent the tissue from falling off the slide.

6. The method according to claim 1, characterized in that In step (3), in the positive ion mode, the spray solvent is acetonitrile: water = 80:20 (V / V), and the flow rate is 7 μL / min; in the negative ion mode, the spray solvent is acetonitrile: water = 80:20 (V / V), and the flow rate is 7 μL / min.

7. The method according to claim 6, characterized in that In step (3), the scanning parameters are set by the mass spectrometry imaging platform control program software, the X-axis scanning speed is 0.2 mm / s, the Y-axis scanning speed is 0.1 mm, the step between scanning lines is 50 μm, and the delay time for each line is 7 s.

8. The method according to claim 1, characterized in that In step (4), the main parameter of the principal component analysis (PCA) model is R2X, and the elliptical area represents the 95% confidence interval. The projection score value of each sample on the plane formed by the first principal component and the second principal component is the spatial coordinate, which can intuitively reflect the similarity or difference between samples. If there is a significant difference between two samples, then the positions of these two coordinate points on the score map are relatively far, and vice versa.

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