Method for visualizing space-time dynamic change of wild corn kernel metabolite by using space metabolome

Abstract

The invention relates to a method for analyzing spatial distribution and change of metabolites in wild corn kernels by utilizing spatial metabolome. The method comprises the steps of material selection, flaking, machine detection, information analysis and data interpretation. According to the method, high-flux visualization of spatial distribution of thousands of metabolites in tissue cells can be realized, and specific metabolites in each interval are qualitatively and quantitatively determined. By utilizing the space metabonomics technology, the distribution of different nutrient substances in different development stages of the wild corn kernels can be better known, and valuable candidate metabolites can be provided for improving the nutrition of the corn kernels and breeding in the future.

Description

Technical Field

[0001] The present invention relates to the field of biological experimental technology and plant metabolite data mining, and specifically to using spatial metabolomics methods to visualize the spatiotemporal dynamic changes of wild corn kernel metabolites and to mining metabolites specifically expressed in different cell tissue regions. Background Art

[0002] Corn, as an important crop, can be traced back to about 9,000 years ago, from a wild plant in southwestern Mexico, teosinte. Through long-term artificial selection and domestication, wild corn gradually evolved into modern corn, a process that significantly increased crop yields and improved its nutritional value. Domesticated corn not only provides more than 60% of calorie intake for humans and animals, becoming the main source of energy consumption, but also, as the main ingredient of feed, plays a key supporting role in the production of meat and protein, thereby indirectly promoting human protein intake.

[0003] During the long process of corn domestication, the intervention of humans and breeders showed obvious tendencies. This tendency not only shaped the modern form of corn, but also greatly reduced the genetic diversity of crops, making many valuable genetic resources not fully utilized. For example, the predecessor of wild corn, Teosinte, had hard and tightly closed husks, which undoubtedly provided strong protection for seeds against drought and pests and diseases, but also limited the spread and consumption of seeds. As humans began to consciously plant and domesticate corn, the husks of wild corn gradually underwent changes. Early farmers may have been more inclined to choose individuals with looser husks that were easy to use. We found that wild corn has no cob, and the husk is not only the outer shell to protect the seeds, but it may also replace some of the functions of the cob and undertake the task of transporting nutrients, which is essential for the growth and development of corn. Despite this, the spatial distribution of metabolites inside the husk remains an unsolved mystery.

[0004] Metabolites are the bridge between genotype and phenotype, playing an important role in plant growth and development and environmental adaptability. However, little is known about how metabolites differentiate during maize domestication and adaptation. Since biological tissues and organs are composed of multiple types of cells and have heterogeneity and complexity, the spatial distribution of metabolites is closely related to changes in their pathophysiological functions, so it is necessary to link the dynamic changes of metabolites with their spatial location distribution. By studying the metabolites contained in the husk, we are expected to reveal new discoveries and insights. However, the traditional metabolomics detection method of chromatography-mass spectrometry often loses the spatial information of metabolites during the homogenization of samples, which makes it impossible to accurately determine which metabolites exist in the husk. Spatial metabolomics is developed based on the principle of mass spectrometry imaging, which can avoid the loss of metabolite spatial information caused by homogenization of samples, thereby realizing the qualitative, quantitative and positional analysis of small molecule metabolites. It is crucial to clarify the synthesis, accumulation and regulation mechanism of metabolites. Summary of the invention

[0005] Currently, little is known about the spatiotemporal distribution of metabolites in wild corn kernels. The present invention uses spatial metabolomics to visualize the spatiotemporal dynamic changes of its metabolites, thereby mining metabolites specifically expressed in different regions, providing a data basis for optimizing the nutritional value of corn kernels and future breeding work.

[0006] The present invention provides a method for qualitatively and quantitatively analyzing the spatiotemporal dynamic changes of wild corn kernel metabolites, comprising the following steps: 1) material selection: selecting wild corn kernels in two periods, namely, cell differentiation and the initial filling period and the filling period, through frozen sections (selecting wild corn kernels with rich metabolite content and complete tissue morphology).

[0007] 2) Embedding and fixation: Place the cleaned kernels into an embedding box, cover the surface completely with embedding glue, and place on dry ice for slow freezing. Add an appropriate amount of embedding glue to the base to fix the sample.

[0008] 3) Sectioning and staining: Place the fixed tissue sample on the sample head of the slicer, cut frozen tissue sections of a set thickness, and adhere and fix them on a positively charged anti-shedding slide. Select one of the sections for toluidine blue staining. The rest are stored in a -80℃ ultra-low temperature refrigerator for subsequent imaging analysis.

[0009] 4) Slice pretreatment: After the frozen tissue slices are taken out from the -80°C ultra-low temperature freezer, they are quickly placed in a vacuum desiccator and vacuum dried at room temperature for about 30 minutes.

[0010] 5) Data collection and analysis: The dried slices were tested on the machine, and the scanning parameters were set by the mass spectrometry imaging platform control program software. Visualization processing, cluster analysis and other data analyses were performed using various software packages such as imzMLConverter, MSiReader and Cardinal.

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

[0012] The present invention has developed a spatial metabolomics method to reveal the spatiotemporal dynamic changes of wild corn kernel metabolites, achieving 1) visualization of the spatial distribution and expression abundance of thousands of metabolites in wild corn kernels; 2) detection of several metabolites unique to the husk and their spatial distribution; 3) cluster analysis using the detected metabolites to obtain accurate cell clustering. All of these provide a wealth of candidate metabolites for optimizing the quality and yield of corn kernels, and then help to explore genes that regulate their related metabolic pathways, which can achieve accurate regulation of target compounds.

[0013] Compared with traditional metabolomics techniques, the advantages of spatial metabolomics include: 1) It integrates a variety of technical means, such as frozen section preparation, direct mass spectrometry detection, and bioinformatics data analysis, which enables researchers to obtain more comprehensive spatial distribution and content information of metabolites in tissue sections. This combination of multiple technologies not only improves the accuracy of the research, but also greatly reduces errors and losses in the experimental process. 2) Mass spectrometry imaging technology avoids the loss of metabolites that may be caused by sample homogenization, so that specific metabolites in tissue sections can be directly captured. For example, for the special structure of husk, mass spectrometry imaging technology can directly study it to help reveal its function and mechanism of action. The advantage of this method is that it can maintain the integrity of the biological tissue structure, thus providing more accurate and in-depth information for research. 3) Mass spectrometry imaging technology does not require complex chemical or radioactive labeling of samples, nor does it require cumbersome pre-treatment steps. This simple, fast and efficient research method greatly saves time and cost, and also improves the efficiency and quality of research. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Staining images of frozen sections of wild corn. a is the cell differentiation stage, b is the cell differentiation and initiation of grain filling, and c is the grain filling stage. b and c show that metabolites begin to accumulate in large quantities at the beginning of grain filling.

[0015] Figure 2 Spatial metabolome sample collection information of wild maize kernels (two periods in total, TEO-12 is the cell differentiation and filling initiation period, TEO-18 is the filling period, three replicates in each period, a total of 6 samples).

[0016] Figure 3 SSCC cluster diagram of TEO-12. a is TEO-12, including 9 regions, namely 1-petal, 2-nucellar tissue, 3-endosperm, 5-seed coat, 6-basal transfer layer, 7-pedicel, 8-noise pollution area, 4, 9-husk; b is TEO-18, including 9 regions, namely 2-endosperm; 3-petal; 4-pedicel; 5-seed coat; 1, 6-husk; 7-noise pollution; 8-aleurone layer.

[0017] Figure 4 Visualized metabolite profile of wild corn kernels. A is the ubiquitously expressed aspartic acid, and b is the husk-specific curcumin glucuronide.

[0018] Figure 5 28 metabolite molecules that are specifically and highly expressed in glumes (mz is the mass-to-charge ratio, Metabolites is the metabolite name, and Cupule is the contribution of the metabolite in the glumes). DETAILED DESCRIPTION

[0019] 1. Determine the optimal material period through frozen sections

[0020] A large number of wild corn kernels were selected for frozen sectioning and staining, and the tissue structure and cytological morphology of the early, middle and late developmental stages were studied and differentiated. It was found that the tissue structure of the kernels in the two stages of cell differentiation and filling initiation and filling was relatively complete, and the metabolite content was relatively rich and different. Finally, it was decided to select these two stages (three replicates in each stage, a total of 6 kernels) for computer and subsequent analysis.

[0021] 2. Sample Embedding and Fixation

[0022] Place the cleaned kernels into an embedding box, cover their surface completely with Lycra embedding gel, and place them on dry ice for slow freezing. Then take the embedded samples out of the embedding box and add an appropriate amount of embedding gel to the base to fix the samples.

[0023] 3. Sectioning and Staining

[0024] Place the fixed tissue sample on the sample head of the slicer, cut frozen tissue slices of set thickness, cut 10 slices in a row, and adhere and fix them on a positively charged anti-shedding slide. Select one of the slices for toluidine blue staining. The rest are stored in a -80℃ ultra-low temperature refrigerator for subsequent imaging analysis.

[0025] 4. Slice preprocessing

[0026] After the frozen tissue sections were taken out from the -80°C ultra-low temperature freezer, they were quickly placed in a vacuum desiccator and vacuum dried at room temperature for about 30 minutes.

[0027] 5. Data Collection and Analysis

[0028] 1) The dried slices were tested on the machine, and the scanning parameters were set by the mass spectrometry imaging platform control program software. (The detection platform is composed of an AFADESI-MSI platform (Beijing Victor Technology Co., LTD, Beijing, China) and a Q-Orbitrap mass spectrometer (Q Exactive, Thermo Scientific, USA) in series)

[0029] 2) Mass spectrometry imaging data acquisition adopts a line-by-line scanning method. According to the size of the tissue slice determined in the early stage, the X-axis and Y-axis scanning lengths (mm) of the scanning area are determined. The scanning speed (Vx, mm / s) can be set in the X-axis direction, and the step between two adjacent scanning lines can be set in the Y-axis (Dy, mm).

[0030] 3) The spray solvent flow rate was 5 μL / min, the transport gas flow rate was 45 L / min, the spray voltage was set to 7 kV, and the distance between the sample surface and the sprayer, and between the sprayer and the ion transfer tube was 3 mm.

[0031] 4) MS resolution was set to 70,000, mass range was 70-1000 Da, and automatic gain control (AGC) was used.

[0032] The AGC target was 2E6, the maximum injection time was set to 200ms, the S-lens voltage was 55V, and the capillary temperature was 350°C. 5) Use imzMLConverter to convert the original ".raw" format file of mass spectrometry acquisition to ".imzML" format, and then import

[0033] MSiReader (an open source software for viewing and analyzing high-resolution mass spectrometry imaging files on the Matlab platform) was imported, and the ion image was reconstructed after background subtraction using the Cardinal software package to visualize various ion imaging images.

[0034] 6) The ions detected by AFADESI were annotated using the pySM spatial metabolome annotation framework and the local metabolite database SmetDB (Lumingbio, Shanghai, China), and a total of 700 metabolites were detected in wild corn kernels.

[0035] 7) The quantitative results of the spatial metabolome are an MxN dimensional matrix, which can usually reach ultra-high dimensions of tens of thousands of rows x thousands of columns. By performing spatial shrunken centroids clustering analysis on such ultra-high-dimensional data with spatial information, SSCC clustering is performed on the basis of calculating the distances between all corresponding neighbors of two pixels, and finally a visualization of the clustering results of the overall expression level is obtained. Among them, 9 partitions were obtained in the TEO-12 period and 8 partitions were obtained in the TEO-18 period.

Claims

1. A method for visualizing the spatiotemporal dynamic changes of wild corn kernel metabolites using spatial metabolomics, characterized in that The following steps are involved: 1) Material selection: Wild corn kernels from two stages, cell differentiation and the beginning of grain filling, and the grain filling stage, were selected through frozen sectioning. 2) Embedding and fixation: Place the cleaned kernels into an embedding box, cover the surface completely with embedding glue, and place on dry ice for slow freezing. Add an appropriate amount of embedding glue to the base to fix the sample. 3) Sectioning and staining: Place the fixed tissue sample on the sample head of the slicer, cut frozen tissue sections of a set thickness, and adhere and fix them on a positively charged anti-shedding slide. Select one of the sections for toluidine blue staining. The rest are stored in a -80℃ ultra-low temperature refrigerator for subsequent imaging analysis. 4) Slice pretreatment: After the frozen tissue slices are taken out from the -80°C ultra-low temperature freezer, they are quickly placed in a vacuum desiccator and vacuum dried at room temperature for about 30 minutes. 5) Data acquisition and data analysis: The dried slices were tested on the machine, and the scanning parameters were set by the mass spectrometry imaging platform control program software. Visualization processing, cluster analysis and other data analyses were performed using various software packages such as imzMLConverter, MSiReader and Cardinal.

2. The method according to claim 1, characterized in that In step (1), frozen sections were used to explore and establish two key time nodes for wild maize for spatial metabolomics experiments, including two developmental stages: cell differentiation and the start of grain filling, and the grain filling stage, which roughly correspond to 12 and 18 days after pollination (DAP) of cultivated maize. The selection criteria were periods when the sections were intact and the metabolite content was rich.

3. The method according to claim 1, characterized in that In step (4), the slices are adhered and fixed to a SuperfrostPlus positively charged anti-shedding slide, the surface of which has a persistent cationic charge, allowing the tissue to be better adsorbed thereon.

4. The method according to claim 1, characterized in that: In step (5), data acquisition was performed using an Xcalibur 4.4.16.14 system, the spray solvent flow rate was 5 μL / min, the transport gas flow rate was 45 L / min, the spray voltage was set to 7 KV, and the distance between the sample surface and the sprayer, and between the sprayer and the ion transfer tube was 3 mm.

5. The method according to claim 4, characterized in that In step (5), the MS resolution was set to 70,000, the mass range was 70-1000 Da, the maximum injection time was set to 200 ms, the S-lens voltage was 55 V, and the capillary temperature was 350 °C.

6. The method according to claim 5, characterized in that In step (5), the ion image is reconstructed by background subtraction using the Cardinal software package, and then the region-specific MS profile is accurately extracted by matching the high spatial resolution sample white film image to obtain the imaging images of ions with different mass-to-charge ratios in all samples.

7. The method according to claim 6, characterized in that In step (5), all detected ions were annotated with substances using the pySM spatial metabolome annotation framework and the local metabolite database SmetDB (Lumingbio, Shanghai, China) to obtain the exact metabolite information.

Images