Three-dimensional reconstruction method suitable for plant root tip single cell space transcriptome

Abstract

The invention provides a plant root tip single cell space transcriptome three-dimensional reconstruction method, which comprises the following steps of: a, performing three-dimensional imaging on a first plant root tip based on an X-ray imaging microscopic technology to obtain X-ray imaging microscopic data; b, capturing and analyzing transcriptomes of one or more frozen sections of a second plant root tip based on Stereo-seq to obtain spatial group data of the second plant root tip; c, extracting a cell nucleus of a third plant root tip, and obtaining snRNA-seq data of the third plant root tip; d, the Stereo-seq data and the snRNA-seq data are subjected to preprocessing, and the Stereo-seq data and the snRNA-seq data obtained after preprocessing are subjected to integration and annotation; and e, performing spatial registration and integration on X-ray imaging microscopic data and the obtained integrated and annotated data to obtain a single cell spatial transcriptome three-dimensional reconstruction graph of the plant root tip.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biotechnology and imaging technology, and specifically relates to a method suitable for spatial transcriptome three-dimensional reconstruction of plant root tip single cells, which is a new method combining X-ray microscopic imaging technology and single cell data and spatial transcriptomics, and is used for constructing three-dimensional gene expression atlas of plant root tip. BACKGROUND

[0002] Plants have perfect organ morphology and magical cell structure, which attract scientists to study to uncover the mystery. Since Robert Hook discovered cork cells nearly 400 years ago, optical microscopes have been the main force in plant biology research. The progress of illumination technology and optical technology, as well as the introduction of fluorescent probes, greatly improve the resolution and contrast. Microscopic images are essentially two-dimensional; confocal optical microscopy allows three-dimensional imaging, but the penetration depth is limited to about 250 μm. However, many organs in plants have symmetrical or asymmetrical structures and cell morphologies, which are difficult to clearly show in two-dimensional images. In particular, the three-dimensional cell shape of some complex organs is more helpful for us to understand plants.

[0003] With the development of technology, X-ray CT scanning is used as a powerful tool to obtain data of complex three-dimensional organ cell structure. Since the attenuation of X-rays depends on density, it is particularly suitable for plant imaging due to the ubiquitous intercellular space in plant organs. X-ray CT scanning generates three-dimensional images of plant organs or tissues from a stack of X-ray images obtained when rotating the sample. Its advantages include: large penetration depth depending on photon energy, scalable field of view and resolution from 500 nm to 1 mm, minimal sample preparation and non-invasive real-time non-destructive imaging. However, existing X-ray CT scanning imaging technology is mostly used for anatomical structure analysis, and lacks the ability to integrate with single cell data and molecular expression information.

[0004] Biological processes are orchestrated and regulated throughout the life cycle by a highly complex system of gene expression. Since the central dogma of molecular biology was proposed, genes have been regarded as the basic molecular units of heredity, determining the development of organisms. In addition, the complexity of organisms is not only determined by the number of genes, but more importantly by gene expression, which is closely related to and dominated by cell types and cell states. Many important discoveries in the field of life sciences have originated from the understanding of cell organization in tissues and their association with specific biological functions. Therefore, scientists have long been working to characterize gene expression profiles during development through various methods. The advent of high-throughput omics technologies has profoundly changed our ability to parse gene expression patterns and shaped our understanding of various aspects of biology. Due to its high-throughput and cost-effective advantages, bulk RNA sequencing (bulk RNA-seq) emerged about a decade ago and has been widely used to assess gene expression across the whole genome. However, bulk RNA-seq has limitations in clearly displaying gene expression from different cell mixtures, which masks the heterogeneity of cells. To address this limitation, methods such as single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have been developed. These methods enable the analysis of gene expression in single cells, allowing the differentiation of different cell types, the discovery of new states, and a deeper understanding of biological processes. However, single-cell transcriptomes often fail to relate gene expression to spatial location of cells, especially in species with limited functional annotation. Recently, Stereo-seq emerged as a powerful technology that can localize gene expression in multicellular organisms. However, there is a need to improve the gene capture efficiency and accuracy of Stereo-seq transcriptomes. Overall, each RNA analysis technology has its advantages and disadvantages. Integrating diverse high-throughput transcriptomic data to generate a unified transcriptomic atlas provides a potential powerful solution for a comprehensive depiction of gene expression in single cells during development. However, integrating different transcriptomic technologies to generate a comprehensive transcriptomic atlas remains a challenging direction of development. Therefore, there is an urgent need for a high-resolution three-dimensional imaging method to integrate the fine anatomical structure of plant organs with spatially resolved gene expression information to study the spatiotemporal regulation mechanisms of plant organ development. SUMMARY

[0005] In view of the above problems, the present application provides a method suitable for three-dimensional reconstruction of single-cell spatial transcriptome of plant root tip, which is a new method combining X-ray microscopy imaging technology and single-cell data and spatial transcriptomics for constructing three-dimensional gene expression atlas of plant root tip.

[0006] Specifically, the present application provides the following technical solutions:

[0007] In one aspect, the present application provides a method for spatial transcriptome 3D reconstruction of plant root tip, comprising the following steps:

[0008] a. 3D imaging of the first plant root tip based on X-ray imaging microscopy, obtaining X-ray imaging microscopy data of the first plant root tip;

[0009] b. capturing and analyzing the transcriptome of one or more frozen sections of the second plant root tip based on Stereo-seq, obtaining spatial group data of the second plant root tip, the spatial group data comprising gene expression data of the second root tip;

[0010] c. extracting the nuclei of the third plant root tip, and obtaining snRNA-seq data of the third plant root tip;

[0011] d. preprocessing the Stereo-seq data obtained in step b and the snRNA-seq data obtained in step c, respectively, and integrating and annotating the preprocessed Stereo-seq data and snRNA-seq data;

[0012] e. spatially registering and integrating the X-ray imaging microscopy data obtained in step a and the integrated and annotated data obtained in step d, to obtain a spatial transcriptome 3D reconstruction map of the plant root tip.

[0013] In some embodiments, the plant is soybean.

[0014] In some embodiments, step a comprises a step of dehydrating and drying the first plant root tip.

[0015] In some embodiments, the dehydrating and drying step comprises:

[0016] (1) 50% formaldehyde-acetic acid-ethanol (FAA) vacuum infiltration until no air bubbles come out from the sample (-0.08 MPa, 4°C) to fix the first plant root tip;

[0017] (2) gradient ethanol dehydration in a centrifuge tube, 50% ethanol→70% ethanol→80% ethanol→90% ethanol→95% ethanol→100% ethanol→100% ethanol→100% ethanol, 15 min for each gradient, placed on ice;

[0018] (3) pour out 100% ethanol, replace with 3:1 volume ratio of ethanol:acetone for 30 min, placed on ice; replace with 3:2 volume ratio of ethanol:acetone for 30 min, placed on ice; replace with acetone for 30 min; repeat acetone replacement for 30 min;

[0019] (4) placing the first plant root tip in a basket in a critical point drying instrument, replacing with liquid CO2, slowly exhausting after warming to 31.1°C, completing critical point drying, obtaining the dehydrated and dried first plant root tip.

[0020] In some embodiments, step b comprises the following steps:

[0021] b1. sample preparation and embedding;

[0022] b2. frozen section preparation;

[0023] b3. RNA quality detection of frozen sections, optionally, RIN≥7 is considered as a qualified sample;

[0024] b4. chip processing;

[0025] b5. tissue fixation;

[0026] b6. tissue permeabilization, optionally, the permeabilization time is 12 minutes;

[0027] b7. in situ reverse transcription reaction;

[0028] b8. tissue removal and product recovery;

[0029] b9. reverse transcription product recovery and amplification;

[0030] b10. reverse transcription product purification;

[0031] b11. reverse transcription product fragmentation and amplification;

[0032] b12. double-fragment screening, optionally, the quality control standard is that the yield is greater than 300 ng and the fragment distribution main peak is between 400-600;

[0033] b13. sequencing, sequencing in double-end mode, each sequencing read contains a 50 bp Read 1 and a 100 bp Read 2, and spatial coordinate information is matched through barcode.

[0034] In some embodiments, step b comprises the following steps:

[0035] b1. sample preparation and embedding, including taking fresh soybean root tips, placing them in embedding agent, placing on ice, vacuumizing, and RNase-free operation throughout;

[0036] b2. frozen section preparation, using a constant temperature freezing microtome to section, with a section thickness of 10 μm;

[0037] b3. RNA quality detection, including total RNA extraction of the section obtained in step b2, confirming quality control, and then performing formal spatiotemporal transcriptome experiment on the section;

[0038] b4. Chip processing, including SSC cleaning of the spatial group sequencing chip and removing RNase with RNase inhibitor, then drying the surface of the spatial group sequencing chip, carefully placing the frozen section on the spatial group sequencing chip, and quickly baking the section at 37°C for 3 min;

[0039] b5. Tissue fixation, including placing the spatial group sequencing chip in a pre-cooled methanol solution at -20°C for fixation, taking the fixed tissue chip out of the methanol, and placing the chip in a culture dish with sealing film; after the methanol evaporates and the chip surface dries, wash the spatial group sequencing chip with SSC and keep the chip surface moist; absorb the liquid on the surface of the chip and immediately proceed to the next step of permeation;

[0040] b6. Tissue permeation treatment, including adding pre-heated and balanced permeation reagent to the spatial group sequencing chip; absorbing the permeation reagent on the surface of the spatial group sequencing chip, washing with SSC; absorbing the liquid on the surface of the spatial group sequencing chip, transferring the spatial group sequencing chip to a new culture dish, and immediately adding the next reaction solution;

[0041] b7. In situ reverse transcription reaction, gently adding the reverse transcription mixture balanced to room temperature to the chip, and reacting in a 42°C incubator;

[0042] b8. Tissue removal and product recovery, absorbing the reverse transcription mixture on the surface of the chip, washing the spatial group sequencing chip with SSC; adding tissue removal buffer, reacting in a 37°C incubator, washing the spatial group sequencing chip with SSC, and proceeding to the next step;

[0043] b9. Reverse transcription product recovery and amplification;

[0044] b10. cDNA purification;

[0045] b11. cDNA fragmentation and amplification;

[0046] b12. Two-stage fragment screening, with a quality control standard of more than 300 ng of yield and a main peak of fragment distribution between 400-600;

[0047] b13. Sequencing preparation, with a library concentration of Qubit quantification ≥ 2 nM, performing sequencing in a double-end mode, each sequencing read containing a 50 bp Read 1 and a 100 bp Read 2, and spatial coordinate information being matched through barcode.

[0048] In some embodiments, in step b6, 100 pl of the preheated equilibrated permeabilization reagent is added dropwise per chip for 12 minutes, and then the permeabilization reagent on the surface of the spatial group sequencing chip is aspirated.

[0049] In some embodiments, the Stereo-seq data preprocessing includes determining coordinate identities by mapping to the coordinates of the in situ capture chip, removing reads with UMI quality scores lower than 10, aligning the remaining reads to a reference genome such as Gmax_ZH13_V2.0 and annotating to gene sets, and finally generating an expression profile matrix containing coordinate identity information for subsequent analysis.

[0050] In some embodiments, when determining coordinate identities by mapping to the coordinates of the in situ capture chip, one mismatch is allowed to tolerate sequencing or PCR errors.

[0051] In some embodiments, when generating the expression profile matrix, due to the fact that the plant tissue only covers part of the surface of each Stereo-seq chip, a custom script is used to extract the exact area of the spatial transcriptome data, align the spatial transcriptome data with the ssDNA image, and then determine the boundary according to the sample edge.

[0052] In some embodiments, when generating the expression profile matrix, due to the characteristic that mRNA is captured at subcellular resolution on a single DNA nanoball, the spatial expression profile matrix is convolved into an N x N size spatial expression profile block and named binN, N = 80, clustering is performed, and the clustering results are projected into two-dimensional space by the "RunUMAP" function. The clustered regions are annotated according to the relative anatomical position.

[0053] In some embodiments, the clustering method is to extract a plurality of, for example, 40 PCs using a principal component analysis (PCA)-based dimensionality reduction algorithm, perform Lovain clustering using the "FindNeighbors" function, and set the resolution to 1.

[0054] In some embodiments, snRNA-seq sequencing is performed in a paired-end mode, and each sequencing read includes a 30 bp Read 1, a 100 bp Read 2 gene sequence, and a 10 bp sample index barcode read.

[0055] In some embodiments, the 30 bp Read 1 includes a 10 bp cell barcode 1, a 10 bp cell barcode 2, and a 10 bp unique molecular identifier.

[0056] In some embodiments, the preprocessing of snRNA-seq data includes filtering raw sequencing reads and demultiplexing by barcode assignment; then, the obtained reads are aligned to a soybean reference genome, e.g., Gmax_ZH13_V2.0, using Spliced Transcripts Alignment to a Reference, annotated to gene sets using PISA, automatically identify valid cells using the “barcodeRanks” function of the DropletUtils package, remove background beads and reads with UMI counts less than 500 based on the UMI number distribution of each cell, then calculate gene expression of cells using PISA and create a gene x cell matrix for each library, merge expression data of different libraries of the same tissue using the “Merge” function of the R package Seurat, followed by standardization using the “NormalizeData” function with default parameters, after logarithmic standardization by the “ScaleData” function, select 1500 high-variation genes using the “FindVariableFeatures” function, in order to subsequent clustering and visualization, extract 40 PCs using the principal component analysis (PCA)-based dimensionality reduction algorithm, perform Lovain clustering using the “FindNeighbors” function with a resolution of 1, project the clustering results to a two-dimensional space using the “RunUMAP” function, and identify differentially expressed genes using the “FindAllMarkers” function.

[0057] In some embodiments, filtering raw sequencing reads includes removing reads with average base quality score less than 4, more than 2 bases with quality score less than 10, containing N bases, or inappropriate barcodes.

[0058] In some embodiments, integrating and annotating the preprocessed Stereo-seq data and snRNA-seq data includes using the “FindTransferAnchors” and “TransferData” functions of the R package Seurat, calculating the proportion of cell clusters determined by snRNA-seq located to each cluster determined by spatial transcriptomics, completing the projection from UMAP of snRNA-seq to cluster embedding of Stereo-seq by the “Knn” function, obtaining the spatial position of clusters determined by snRNA-seq, and annotating them according to their anatomical structure, and identifying other cell clusters based on the lineage relationship of previously annotated cell clusters using the R package Clustertree.

[0059] In some embodiments, the spatial registration and integration of the X-ray imaging microscopic data obtained in step a and the integrated and annotated data obtained in step d comprises: extracting tissue images in the circular field of view of the serial section pictures using the Segment Editor module in 3D Slicer; based on the extracted tissue images of the serial sections, constructing a 3D Mesh of the outer contour of the tissue using 3D Slicer, and smoothing the 3D Mesh, and storing the final 3D Mesh in obj format; based on the first 40 principal components, clustering the single-cell transcriptome data using the Louvain algorithm (resolution = 1) to identify the cell type characteristics of the tissue; mapping the Stereo-seq data onto the single-cell data using the FindTransferAnchors and TransferData functions in Seurat, and determining the cell type of each Squarebin of the Stereo-seq data according to the mapping score; calculating the Z-axis position of the corresponding 3D Mesh of the Stereo-seq according to the section position in the Stereo-seq serial section experiment record; using the TrakEM2 elastic registration algorithm in ImageJ to register the Stereo-seq serial sections with the X-ray images at the corresponding Z-axis positions, thereby determining the spatial position of the Stereo-seq serial sections in the 3D Mesh; and finally displaying the information such as gene expression and tissue type information on the Stereo-seq serial sections at the accurate spatial positions in the tissue 3D Mesh, thereby obtaining a three-dimensional reconstruction map of the plant root tip single-cell spatial transcriptome.

[0060] In another aspect, the present application provides a dehydration drying method suitable for X-ray imaging of plant root tips, comprising the following steps:

[0061] (1) vacuum infiltration of 50% formaldehyde-acetic acid-ethanol (FAA) (-0.08 MPa, 4°C) until no air bubbles are emitted from the sample, and fixation of the plant root tips;

[0062] (2) gradient ethanol dehydration in a centrifuge tube, 50% ethanol→70% ethanol→80% ethanol→90% ethanol→95% ethanol→100% ethanol→100% ethanol→100% ethanol, each gradient for 15 min, placed on ice;

[0063] (3) pour out 100% ethanol, replace with 3:1 volume ratio of ethanol:acetone for 30 min, placed on ice; replace with 3:2 volume ratio of ethanol:acetone for 30 min, placed on ice; replace with acetone for 30 min; repeat acetone replacement for 30 min;

[0064] (4) Put the plant root tips in the basket and place them in the critical point drying instrument. Replace with liquid CO2, slowly exhaust after warming to 31.1°C, complete critical point drying, and obtain dehydrated and dried plant root tips.

[0065] Definitions

[0066] X-ray imaging technology: an advanced characterization technology based on the penetration of X-rays for three-dimensional imaging. It is a non-destructive 3D imaging technology that can reveal the complex structure inside the sample.

[0067] RIN: full name for RNA integrity number, is an important indicator for judging RNA integrity. The highest RIN value is 10, representing the very good integrity of RNA, and the lowest value is 0, representing the very poor integrity of RNA.

[0068] Neuroglancer: a high-performance WebGL viewer and visualization framework developed by Google Connectomics team, used to view three-dimensional volume data. It supports multiple data sources and can display arbitrary (non-axis-aligned) volume data slice views, as well as 3D network and line segment models (skeletons).

[0069] HDF5: a file format that supports hierarchical data storage, allowing users to store multiple datasets and metadata in the same file.

[0070] Metadata: information describing the properties of data, used to support functions such as indicating storage location, historical data, resource search, file record, etc.

[0071] 3D Slicer: an open-source software for image analysis, visualization and processing. It supports multiple imaging modalities and provides powerful three-dimensional reconstruction, segmentation, registration and quantitative analysis functions.

[0072] Registration: refers to the matching of geographical coordinates of different images obtained by different imaging methods in the same area, including geometric correction, projection transformation and unified scale processing.

[0073] TrakEM2 software: a program for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation.

[0074] Coordinate mapping: refers to the process of converting a point or a set of points from one coordinate system to another in two-dimensional or three-dimensional space.

[0075] Spatial transcriptome data can provide high-throughput gene expression profiles and spatial structure of tissues simultaneously. Cell clustering is an important step in spatial transcriptome data analysis and the basis for downstream in-depth analysis. Cell clustering is a process of dividing a cell collection into subsets, and each subset is a cluster, so that the objects in the cluster are similar to each other, but different from the objects in other clusters.

[0076] Advantages

[0077] The method of the present application has the following advantages:

[0078] 1. Non-destructive: X-ray imaging avoids physical sectioning, preserving sample integrity for subsequent dynamic observation.

[0079] 2. The present application solves the problem of spatial group two-dimensional image needing a three-dimensional cell structure support to construct a gene expression-structure correlation atlas.

[0080] 3. Cross-scale integration: based on the resolution of spatial group data and cell clustering, the present application supports cross-scale data correlation from the whole organ to the single cell level.

[0081] 4. Improved sample preparation and X-ray imaging technology provides a powerful method for imaging the cell tissue of soybean root tips.

[0082] 5. Although Stereo-seq enables sequencing-based spatial transcriptomics to measure gene expression levels in the entire transcriptome at subcellular resolution, it is still difficult to reconstruct organ 3D single cell expression profiles in current research on plants. The present application combines X-ray imaging technology with Stereo-seq to obtain 3D single cell expression profiles, which is conducive to displaying spatial group gene expression profiles from a new perspective.

[0083] 6. Spatial group data is less but has spatial positioning, single cell data is larger, cell clustering is more explicit, but cells lose spatial positioning information, and it is difficult to annotate cell groups. Integrating the advantages of both single cell data and spatial group data, combined with the overall plant root tip cytology characteristics of X-ray, a 3D single cell spatial group atlas of plant root tips can be constructed. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 The X-ray imaging results of the soybean root tip are shown in the figure. A: 3D view of the whole root tip. B: 3D cell display inside the root tip. C: Cell morphology display of the longitudinal section of the root tip. D: Cell morphology display of the stem cell region selected in C. E: Cell morphology display of the transverse section of the root tip. F: Cell morphology display of the stem cell region selected in E.

[0085] Figure 2Spatial group slice information of root tip is shown.

[0086] Figure 3 Spatial group data quality assessment result of root tip is shown.

[0087] Figure 4 Overall arrangement of Stereo-seq based root tip spatial group subpopulation data is shown, different colors represent different cell populations, with clear spatial positioning.

[0088] Figure 5 Single cell population positioning of Stereo-seq based root tip spatial group subpopulation data is shown, single cell population has clear spatial positioning. A: spatial subpopulation positioning map based on Stereo-seq data. The resolution is set to bin80. B: cluster bin count statistics of spatial group ten cell populations, where cell populations I-VII have clear positioning and sufficient bin number.

[0089] Figure 6 Cell subpopulation and annotation based on snRNA-seq data are shown. One dot represents one nucleus, a total of 10198 nuclei, and a total of 49277 soybean coding genes are detected, with an average of 1111 mRNA and 828 genes per nucleus.

[0090] Figure 7 Specific marker genes of each cell population of snRNA-seq data are shown.

[0091] Figure 8 Spatial positioning of single cell population of root tip snRNA-seq data is shown.

[0092] Figure 9 Combined spatial positioning of total cell population of root tip snRNA-seq data is shown.

[0093] Figure 10 Registration and integration based on soybean root tip image and spatial group data are shown, and root tip spatial group data can be queried from 3D space of cell subpopulation, gene expression and cell positioning. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with specific examples and with reference to the drawings.

[0095] Example 1 Construction of Soybean Root Tip Three-dimensional Spatial Transcriptome

[0096] I. X-ray imaging

[0097] Plant root tips are very young tissues with very high water content, while X-ray imaging requires samples to be fully dried, and the cell structure and morphology are very complete, without collapse, so that clear images at the cellular level can be obtained by X-ray for sample analysis.

[0098] The inventors found through searching the prior art that X-ray imaging is mostly used for imaging animal tissues, and there is no report on imaging plant root tips. To solve the problem of plant root tip sample preparation, the inventors found that the following method can be used for the preparation of plant root tip samples, and the required X-ray three-dimensional images were obtained.

[0099] X-ray three-dimensional image scanning is rarely used in the field of plants. Currently, time is used to calculate the scanning process, which is relatively long, resulting in a cost of about 20-40 thousand yuan RMB for obtaining X-ray three-dimensional images of one sample. The sample preparation method of the present application can save a lot of time and cost for other researchers to conduct related research.

[0100] In addition, although some plant samples in the prior art have applied X-ray microscopic scanning technology, they are all whole imaging of plant organs, and the outline of the whole organ can be seen, but due to the absence of sample preparation process, the ultra-high resolution of the X-ray microscope has not been realized in plant sample applications, and further viewing of the structure and morphology at the cellular level is not possible (see, for example, Teramoto et al. High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography, Plant Methods, 2020 16:66). The preparation method of the present application can make the resolution of X-ray imaging reach the cellular level, and one sample preparation can obtain multi-level high-definition images of plant samples from the whole organ level to the cellular resolution.

[0101] Root tip sample preparation: 50% formaldehyde-acetic acid-ethanol (FAA) vacuum infiltration (-0.08 MPa, 4°C) until no air bubbles were observed, then the samples were fixed overnight, and dehydrated in a gradient ethanol (50%→70%→80%→90%→95%→100%) in 15 ml centrifuge tubes, with three 100% to ensure complete dehydration, each for 15 min on ice. The 100% ethanol was replaced with 3:1 volume ratio of ethanol:acetone for 30 min on ice, 3:2 volume ratio of ethanol:acetone for 30 min on ice, and acetone for 30 min. The samples were placed in a sample basket in a critical point drying instrument, and liquid CO2was used for replacement (flow rate 10 L / min for 1 h). After the temperature was increased to 31.1°C (supercritical state), the gas was slowly released, and the critical point drying was completed.

[0102] The X-ray microscope used for imaging was Zeiss Xradia 620 (voltage 30-160 kV, 25 W; spatial resolution 0.5 microns; 0.4x, 4x, 20x, 40x objective) from Carl Zeiss Management GmbH.

[0103] II. Spatially resolved sequencing

[0104] 1. Sample preparation and embedding

[0105] Fresh soybean root tips were taken and placed in OCT embedding medium on ice, and vacuumed for 10 minutes. The OCT embedding medium was used for directional embedding in an embedding box, and the sample was frozen on dry ice. The whole process was performed without RNase.

[0106] 2. Cryosection preparation

[0107] The cryostat microtome was set at -20°C, and the section thickness was 10 microns.

[0108] 3. RNA quality detection

[0109] Twenty 10-micron-thick tissue sections were stored in 1.5-ml EP tubes pre-cooled at -20°C, and total RNA extraction (Beijing Huayueyang Bioengineering, 0416) and quality detection (RIN≥7 was considered as a qualified sample) were performed. The remaining tissue was continuously embedded with OCT, and after the quality detection was confirmed, the sections were subjected to formal spatiotemporal transcriptome experiment.

[0110] 4. Chip processing

[0111] The reagents and consumables used were provided by Huada (Stereo-seq Transcriptome Kit T, 201KT13114; STOmics Accessory Kit, 1000033700; Stereo-seq PCR Adaptor, 301AUX001).

[0112] Carefully pick up the spatial transcriptome chip (Stereo-seq Chip T Carrier (1 cm *1 cm, Cat# 200CT13114, see Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Li, W., Xu, J., et al. (2022). S patiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays. Cell 185:1777-1792. https: / / doi.org / 10.1101 / 2021.01.17.427004) with tweezers and place it in a new 24-well plate. Wash twice with 400 μl of 0.1x SSC; cover the chip surface with 100 μl of NF-H20 (with RNase inhibitor) for 1 min to remove RNase, and then rinse once with 400 μl of NF-H20 (without RNase inhibitor); dry the chip surface, and then prepare the patch. Carefully cover the frozen section on the chip and quickly bake the section at 37 °C for 3 min.

[0113] 5. Tissue fixation

[0114] Place the chip in a pre-cooled methanol solution at -20 °C for 30 min; remove the fixed tissue chip from the methanol and place the chip in a culture dish with parafilm; after the methanol evaporates and the chip surface becomes dry, wash the chip once with 100 μl / chip of 0.1x SSC and keep the chip surface moist; after the 0.1x SSC is absorbed, take a 1 / 20 RI fluorescence photo, select the FITC channel on the microscope, use 10x magnification, and take a full photo. Figure 2 After the liquid on the chip surface is absorbed, add 100 μl / chip of 0.1x SSC, absorb the liquid on the chip surface, and immediately proceed to the next step of permeabilization.

[0115] 6. Tissue permeabilization

[0116] Freshly prepare the permeabilization reagent 0.01N HCl (100 μl 0.1N HCl + 900 μl NF-H2O), prepare the dilution of permeabilization enzyme according to the number of chips, each chip needs 1.1 μl permeabilization enzyme added into 110 μl 0.01N HCl, mix by pipetting, not by vortex. Put the prepared permeabilization reagent into 37°C incubator for 3 minutes; transfer the chip's petri dish into 37°C incubator, add 100 μl / chip of the pre-heated permeabilization reagent, incubate for 12 minutes; aspirate the permeabilization reagent on the chip's surface, add 100 μl / chip of 0.1 x SSC; aspirate the liquid on the chip's surface, transfer the chip into a new petri dish, immediately add the next reagent.

[0117] 7. In situ reverse transcription reaction

[0118] Gently add 100 μl of RT Mix equilibrated to room temperature to the chip, incubate in 42°C incubator for 1 hour.

[0119] 8. Tissue removal and product recovery

[0120] Aspirate the RT Mix on the chip's surface, wash the chip once with 0.1 x SSC; add the tissue removal buffer, incubate in 37°C incubator for 30 minutes. Wash the chip with 0.1 x SSC, repeat twice for the next reaction.

[0121] 9. RT product recovery and amplification

[0122] cDNA release: add 400 μl / well of cDNA Release Mix to the chip, seal and incubate in 55°C incubator for 3 hours, shake every hour.

[0123] cDNA recovery: collect the liquid in the reaction well into a new 1.5 ml centrifuge tube, add 350 μl / well of NF-H2O to wash the chip and collect the liquid.

[0124] 0.8 x beads purification: mix the collected liquid with VAHTSTM DNA Clean Beads equilibrated to room temperature, vortex to mix, incubate at room temperature for 10 minutes; after a brief centrifugation, place the EP tube on a magnetic stand for 3 minutes, remove the supernatant when the liquid is clear; add 1 ml of freshly prepared 80% ethanol (freshly prepared), stand for 30 seconds; discard the supernatant and repeat the previous steps; discard the supernatant and air dry at room temperature for 5 minutes; add 42 μl of NF-H2O to dissolve, mix and stand at room temperature for 5 minutes, briefly centrifuge, stand on the magnetic stand for 3-5 minutes, collect 42 μl of supernatant when the liquid is clear.

[0125] Prepare PCR Mix: 42 μΐ of the above recovered sample, supplemented with NF-H20 to make up the volume to 42 μΐ, add 58 μΐ of PCR Mix, total volume 100 μΐ, and perform PCR reaction; determine the concentration of PCR product.

[0126] 10. cDNA purification

[0127] Mix the PCR reaction solution with VAHTSTM DNA Clean Beads (VAZYME), shake to mix, incubate at room temperature for 10 min; after centrifugation, place on a magnetic stand for 3 min, remove the supernatant; add 1 ml of 80% ethanol, stand for 30 s; discard the supernatant and repeat the steps; discard the supernatant and dry at room temperature for 3 min; add 42 μΐ of NF-H20 to restore the volume, mix well and stand for 5 min, centrifuge briefly, place on a magnetic stand for 3 min, and recover the supernatant; take 1 μΐ of cDNA sample to detect the concentration and cDNA fragment distribution.

[0128] 11. cDNA fragmentation and amplification

[0129] Prepare the fragmentation and amplification system, mix 100 μΐ of PCR amplification product and VAHTSTM DNA Clean Beads (VAZYME), shake to mix, incubate at room temperature for 5 min; after brief centrifugation, place on a magnetic stand for 3 min, transfer the supernatant; mix 20 μΐ of VAHTSTM DNA Clean Beads with the supernatant, shake to mix, incubate at room temperature for 5 min; after brief centrifugation, place on a magnetic stand for 3-5 min, aspirate and discard the supernatant; add 200 μΐ of 80% ethanol, stand for 30 s, aspirate and discard the supernatant carefully; centrifuge briefly, separate on a magnetic stand, and aspirate the liquid at the bottom of the tube; dry the magnetic beads at room temperature for 5 min; add 20 μΐ of NF-H20 to restore the volume, stand at room temperature for 5 min, centrifuge briefly, place on a magnetic stand for 3 min, and transfer the supernatant to a new PCR tube; take 1 μΐ of the screened product to detect the concentration.

[0130] 12. Two-stage fragment screening

[0131] The screened product is detected for fragment distribution using Agilent 2100 High Sensitivity Chip. Quality control standard: yield greater than 300 ng, main peak of fragment distribution between 400-600.

[0132] 13. Sequencing preparation

[0133] Library concentration: Qubit quantification > 2 nM; Sequencing on DNBSEQ T1 sequencing platform, MGISEQ-2000 of BGI-research or DNBSEQ-T7 sequencing platform (Beijing) in paired-end mode. Each sequencing read contains a 50 bp Read 1 and a 100 bp Read 2. Spatial coordinate information is matched by barcode.

[0134] 14. Stereo-seq data preprocessing

[0135] Coordinate identities (CIDs) were determined by mapping to the coordinates of the in situ capture chip (allowing one mismatch to tolerate sequencing or PCR errors). After removing reads with UMI quality scores below 10, the remaining reads were aligned to the reference genome (Gmax_ZH13_V2.0) and annotated to gene sets (version 2.1). Finally, a transcriptome matrix containing CID information was generated for subsequent analysis. Since the plant tissue only covered part of the surface of each Stereo-seq chip, a custom script was used to extract the precise area of spatial transcriptome data. To this end, the spatial transcriptome data was aligned to the ssDNA image, and then the boundary was determined according to the sample edge (unequally spaced straight lines arranged irregularly on the surface of the Stereo-seq chip, without planting DNA oligonucleotides for mRNA capture). Due to the feature that mRNA is captured on a single DNA nanoball (DNB) at a subcellular resolution (500 nm), the spatial transcriptome matrix was convolved into N x N size spatial transcriptome blocks (named binN, N = 80 spikes). The clustering method of convolved data is the same as that of snRNA-seq data preprocessing. Finally, the clustering results were projected into two-dimensional space by the "RunUMAP" function. The clustered regions were annotated according to the relative anatomical position.

[0136] III. Single nucleus sequencing

[0137] 1. Soybean nucleus extraction

[0138] Fresh root tip tissue harvested from soybeans was immediately minced in a Petri dish with a scalpel blade and 2 ml of pre-chilled homogenization buffer (Sigma, CELLYTPN1-1KT) was added. Each 1 ml aliquot of the mixture was then transferred to a 1 ml Dounce homogenizer (Kimble), chilled in the refrigerator, and homogenized 10–20 times. The homogenized mixture was then filtered through a 40 μm cell strainer into a 1.5 ml centrifuge tube and centrifuged at 1260 g for 5 minutes at 4°C to pellet the nuclei. The nuclei were then resuspended in blocking buffer containing 1% bovine serum albumin (BSA) and 0.2 U / μL of RNase inhibitor. Finally, the nuclei were again pelleted by centrifugation at 600 × g for 5 minutes at 4°C and resuspended in 1× phosphate-buffered saline (PBS) containing 1% BSA for subsequent library preparation.

[0139] 2. snRNA-seq Library Construction and Sequencing

[0140] snRNA-seq libraries were constructed using the DNBelab C Series High-Throughput Single-Cell System (BGI-research). The snRNA-seq library preparation steps included single nuclei suspension by titrating microfluidics encapsulation, emulsion disruption, mRNA capture bead collection, reverse transcription, cDNA amplification, and purification. The resulting cDNA product was fragmented into 250-400 bp fragments. Indexed sequencing libraries were constructed using the Stereo-seq Library Construction Kit (BGI-Research, 101KL114) according to standard protocols and quantified using an Agilent Bioanalyzer 2100 and the Qubit single-stranded DNA (ssDNA) Assay Kit (Thermo Fisher Scientific). Sequencing was performed in paired-end mode on the BGI-research MGISEQ-2000 or DNBSEQ-T7 sequencing platforms (Beijing). Each sequencing read consists of a 30 bp Read 1 (including 10 bp cell barcode 1, 10 bp cell barcode 2, and a 10 bp unique molecular identifier (UMI), a 100 bp Read 2 gene sequence, and a 10 bp sample index barcode read.

[0141] 3. snRNA-seq Data Preprocessing

[0142] snRNA-seq data were processed using open source software (https: / / github.com / MGI-tech-bioinformatics / DNBelab_C_Series_HT_scRNA-analysis-software) to obtain read count matrix for each gene and each cell. First, raw sequencing reads were filtered (reads with average base quality score lower than 4, more than 2 bases with quality score lower than 10, containing N base or inappropriate barcodes were removed) and demultiplexed by barcodes assignment. Then, obtained reads were aligned to soybean reference genome (Gmax_ZH13_V2.0) using Spliced Transcripts Alignment to a Reference (STAR; v2.7.9) and annotated to gene sets using PISA (version 2.1). Valid cells were automatically identified using the “barcodeRanks” function of DropletUtils tool based on the UMI number distribution of each cell to remove background beads and cells with UMI counts less than 500. Gene expression of cells was then calculated using PISA and gene x cell matrix was created for each library. Expression data of different libraries from the same tissue were merged using the “Merge” function of R package Seurat (v4.1.0) and subsequently normalized using the “NormalizeData” function with default parameters. After log-normalization by the “ScaleData” function, 1500 highly variable genes were selected using the “FindVariableFeatures” function. For subsequent clustering and visualization, 40 PCs were extracted using principal component analysis (PCA)-based dimensionality reduction algorithm, and Lovain clustering was performed using the “FindNeighbors” function with resolution set to 1. The clustering result was projected to two-dimensional space using the “RunUMAP” function. Differentially expressed genes were identified using the “FindAllMarkers” function.

[0143] IV. Integration and annotation of single-nuclei sequencing data and spatially-resolved sequencing data

[0144] Cell types were annotated by integrating snRNA-seq data and Stereo-seq data. First, we used the functions “FindTransferAnchors” and “TransferData” of R package Seurat (v4.1.0) to calculate the proportion of cell clusters determined by snRNA-seq that were located to each cluster determined by spatial transcriptomics. Projection from UMAP of snRNA-seq to cluster embedding of Stereo-seq was done by the function “Knn”. In this way, we obtained the spatial location of clusters determined by snRNA-seq and annotated them according to their anatomical structure. Some other cell clusters were identified based on the lineage relationship of previously annotated cell clusters using R package Clustertree (v1.8.7).

[0145] V. Construction of 3D single-cell spatial group expression profile of root tip

[0146] We used X-ray microscopy for multiscale high-resolution imaging (1.5 microns) as a reference to reconstruct the 3D structure of the sample. The tissue images in the circular field of view of the continuous scanning pictures were extracted using the Segment Editor module in 3D Slicer (v5.8.1). Based on the extracted tissue images of the continuous slices, we used 3D Slicer to construct the 3D Mesh of the tissue outer contour and smooth it, and finally stored the 3D Mesh as an obj format. Based on the first 40 principal components, we used the Louvain algorithm to cluster the single-cell transcriptome data (resolution = 1) to identify the cell type characteristics of the tissue. We used the FindTransferAnchors and TransferData functions in Seurat (v4.1.0) to map the Stereo-seq data to the single-cell data, and according to the mapping score of each Squarebin of Stereo-seq data, we determined its cell type. According to the slice position in the Stereo-seq continuous slice experiment record, we calculated the Z-axis position of the corresponding 3D Mesh of Stereo-seq. Using the TrakEM2 elastic registration algorithm in ImageJ (v1.53c), we registered the Stereo-seq continuous slices with the X-ray images of the corresponding Z-axis position, so as to determine the spatial position of the Stereo-seq continuous slices in the 3D Mesh. Finally we can show the gene expression and tissue type information on the Stereo-seq continuous slices at the accurate spatial position in the tissue 3D Mesh.

[0147] Results and analysis

[0148] First, we obtained the cell images of the whole organ of soybean root tip by X-ray microscopy technology Figure 1), and then we performed serial sectioning of the whole root tip and picked 23 frozen sections for spatially-resolved transcriptomic profiling (Fig. 1a) Figure 2 ), and after deconvolution, the root tip spatial transcriptome contained 24,008 bins, with 1,203 mRNAs detected per bin, 882 genes per bin, and a total of 31,416 genes detected (Fig. 1b) Figure 3 ). Based on spatial localization and gene expression patterns, the Stereo-seq spatially-resolved transcriptomic data yielded ten cell groups (Fig. 1c) Figure 4 ). Seven of these cell groups showed clear spatial localization (Fig. 1d) Figure 5 ). Single-nuclei sequencing of the same batch of samples yielded a total of 10,198 nuclei, which were classified into 25 cell types by snRNA-seq data analysis pipeline (Fig. 2a) Figure 6 ). Most of the cell groups had cell-specific marker genes, indicating that the classification was clear and reasonable (Fig. 2b) Figure 7 ). Among the 25 cell types, 16 were mapped to the spatial clusters, with specific cell localization, and thus we annotated the single-cell groups based on spatial localization (Fig. 2c) Figure 8 and 6 ). The merging of the cell groups with clear localization showed clear localization of the root tip cell groups (Fig. 2d) Figure 9), and obtain the spatial atlas of single cells in the root tip. Further, the SegmentEditor module in 3D Slicer (v5.8.1) was used to extract the tissue images in the circular field of view of the continuous scanning pictures. Based on the extracted tissue images of the continuous sections, a 3D Mesh of the tissue outer contour was constructed using 3D Slicer, and was smoothed, and the final 3D Mesh was stored in the obj format. Based on the first 40 principal components, the Louvain algorithm was used to cluster the single cell transcriptome data (resolution = 1) to identify the cell type characteristics of the tissue. The FindTransferAnchors and TransferData functions in Seurat (v4.1.0) were used to map the Stereo-seq data to the single cell data, and according to the mapping score of each Squarebin in the Stereo-seq data, the cell type was determined. According to the section position in the Stereo-seq continuous section experiment record, the Z-axis position of the Stereo-seq corresponding 3D Mesh was calculated. The TrakEM2 elastic registration algorithm in ImageJ (v1.53c) was used to register the Stereo-seq continuous section with the X-ray image at the corresponding Z-axis position, so as to determine the spatial position of the Stereo-seq continuous section in the 3D Mesh. Finally, we can display the gene expression and tissue type information on the Stereo-seq continuous section at the accurate spatial position in the tissue 3D Mesh.

[0149] We constructed a 3D model of single cell data of the root tip based on the tissue cell model of the X-ray microscope and the section spatial group data information of Stereo-seq, and these data were stored in the Soybean Organ Transcriptome Atlas (SOTA) (https: / / db.cngb.org / stomics / soybean / root.3d / ). This model allows users to explore the composition of the cell population and gene expression patterns in the root tip in three dimensions (https: / / db.cngb.org / stomics / soybean / root.3d / ). Figure 10 ).

[0150] REFERENCES

[0151] Fan J., Shen Y., Chen C., Chen X., Yang X., Liu H., Chen R., Liu S., Zhang B., Zhang M., Zhou G., Wang Y., Sun H., Jiang Y., Wei X., Yang T., Liu Y., Tian D., Deng Z., Xu X., Liu X., and Tian Z. (2025). A large-scale integrated transcriptomic atlas for soybean organ development. Mol. Plant. 18, 669-689.

[0152] The above-described embodiments of the present application are merely intended to further illustrate the purposes, technical solutions and beneficial effects of the present application, and should not be used to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for three-dimensional reconstruction of the spatial transcriptome of a single cell of a plant root tip (e.g., a soybean root tip), characterized in that: The method comprises the following steps: a three-dimensional imaging of the root tip of the first plant based on X-ray imaging microscopy technology to obtain X-ray imaging microscopy data of the root tip of the first plant; b based on Stereo-seq capture and analysis of the transcriptome of one or more frozen sections of the second plant root tip to obtain the spatial group data of the second plant root tip, the spatial group data including the gene expression data of the second root tip; c extracting the nuclei of the root tip of the third plant and obtaining snRNA-seq data of the root tip of the third plant; d. The Stereo-seq data obtained in step b and the snRNA-seq data obtained in step c are preprocessed, and the preprocessed Stereo-seq data and snRNA-seq data are integrated and annotated; e. Spatial registration and integration of the X-ray imaging microscopy data obtained in step a and the integrated and annotated data obtained in step d are performed to obtain a three-dimensional reconstruction of the spatial transcriptome of the plant root tip.

2. The method according to claim 1, characterized in that Step a comprises the step of dehydrating and drying the first plant root tip. Optionally, the dehydrating and drying step comprises: (1) Fix the root tip of the first plant by vacuum infiltration with 50% formaldehyde-acetic acid-ethanol (FAA) until no bubbles emerge from the sample (-0.08 MPa, 4°C); (2) Dehydrate in a centrifuge tube using gradient ethanol: 50% ethanol → 70% ethanol → 80% ethanol → 90% ethanol → 95% ethanol → 100% ethanol → 100% ethanol → 100% ethanol, with each gradient lasting 15 min. Place on ice. (3) Pour out the 100% ethanol and replace it with ethanol:acetone in a volume ratio of 3:1 for 30 minutes, place on ice; replace it with ethanol:acetone in a volume ratio of 3:2 for 30 minutes, place on ice; replace it with acetone for 30 minutes; repeat the acetone replacement for 30 minutes; (4) The first plant root tip is placed in a basket and placed in a critical point drying apparatus, liquid CO2 is replaced, the temperature is raised to 31.1°C and the gas is slowly exhausted to complete critical point drying, and the dehydrated first plant root tip is obtained.

3. The method according to claim 1 or 2, characterized in that Step b includes the following steps: b1. Sample preparation and embedding; b2. Frozen section preparation; b3. RNA quality testing of frozen sections. Optional: RIN ≥ 7 is considered acceptable. b4. Chip processing; b5. Tissue fixation; b6 tissue permeabilization, optionally, the permeabilization time is 12 minutes; b7. In situ reverse transcription reaction; b8. Tissue removal and product recovery; b9. Reverse transcription product recovery and amplification; b10. Purification of reverse transcription products; b11. Reverse transcription product interruption and amplification; b12 double fragment screening, optionally, the quality control standard is a yield greater than 300ng, the main peak of the fragment distribution is between 400-600; b13. Sequencing: Perform paired-end sequencing. Each sequencing read contains a 50 bp Read 1 and a 100 bp Read 2, and the spatial coordinate information is matched by barcode.

4. The method according to any one of claims 1 to 3, characterized in that Stereo-seq data preprocessing includes determining coordinate identifiers by mapping to the coordinates of the in situ capture chip, removing reads with UMI quality scores lower than 10, aligning the remaining reads to a reference genome, such as Gmax_ZH13_V2.0, and annotating them to gene sets. Finally, an expression matrix containing coordinate identifier information is generated for subsequent analysis. Optionally, when determining coordinate identifiers by mapping to the coordinates of the in situ capture chip, one mismatch is allowed to tolerate sequencing or PCR errors.

5. The method according to claim 4, characterized in that When generating the expression matrix, since plant tissue only covers a portion of the surface area of ​​each Stereo-seq chip, a custom script is used to extract the precise region of the spatial transcriptome data. The spatial transcriptome data is aligned with the ssDNA image, and then the boundaries are determined based on the sample edges. Optionally, when generating the expression matrix, due to the characteristic of mRNA being captured at subcellular resolution on a single DNA nanosphere, the spatial expression matrix is ​​convolved into N×N spatial expression profile blocks and named binN, with N=80 root tips. Clustering is performed, and the clustering results are projected into two-dimensional space using the "RunUMAP" function. The clustered regions are annotated according to their relative anatomical position. Optionally, the clustering method is to use a dimensionality reduction algorithm based on principal component analysis (PCA) to extract multiple, for example 40 PCs, and use the "FindNeighbors" function to perform Lovain clustering with a resolution of 1.

6. The method according to any one of claims 1 to 5, characterized in that snRNA-seq sequencing was performed in paired-end mode, and each sequencing read included a 30 bp Read 1, a 100 bp Read 2 gene sequence, and a 10 bp sample index barcode read. Optionally, the 30 bp Read 1 included a 10 bp cell barcode 1, a 10 bp cell barcode 2, and a 10 bp unique molecular identifier.

7. The method according to any one of claims 1 to 6, characterized in that The preprocessing of snRNA-seq data includes filtering the raw sequencing reads and demultiplexing them by barcode assignment; then, the obtained reads are aligned to the soybean reference genome, such as Gmax_ZH13_V2.0, using Spliced ​​Transcripts Alignment to a Reference, and annotated to gene sets using PISA. The "barcodeRanks" function of the DropletUtils tool is used to automatically identify valid cells. The background beads and reads with less than 500 UMI counts are removed based on the distribution of the number of UMIs per cell. PISA is then used to calculate the gene expression of the cells and create a gene × cell matrix for each library. The expression data of different libraries of the same tissue are merged using the "Merge" function of the R package Seurat, and then normalized using the "NormalizeData" function with default parameters. After logarithmic normalization with the "ScaleData" function, the expression data are normalized using the "F The "indVariableFeatures" function was used to select 1500 highly variable genes. For subsequent clustering and visualization, a dimensionality reduction algorithm based on principal component analysis (PCA) was used to extract multiple, for example, 40 PCs. Lovain clustering was performed using the "FindNeighbors" function with a resolution of 1. The clustering results were projected into a two-dimensional space using the "RunUMAP" function. The differentially expressed genes were identified using the "FindAllMarkers" function. Optionally, the raw sequencing reads were filtered, including removing reads with an average base quality score lower than 4, more than two bases with a quality score lower than 10, and reads containing N bases or inappropriate barcodes.

8. The method according to any one of claims 1 to 7, characterized in that The pre-processed Stereo-seq data and snRNA-seq data were integrated and annotated, including using the "FindTransferAnchors" and "TransferData" functions of the R package Seurat to calculate the proportion of cell clusters determined by snRNA-seq localized to each cluster determined by spatial transcriptomics. The "Knn" function was used to complete the cluster embedding from the UMAP projection of snRNA-seq to Stereo-seq, obtain the spatial position of the clusters determined by snRNA-seq, and annotate them according to their anatomical structure. The R package Clustertree was used to identify other cell clusters based on the lineage relationships of previously annotated cell clusters.

9. The method according to any one of claims 1 to 8, characterized in that The X-ray imaging microscopy data obtained in step a and the integrated and annotated data obtained in step d are spatially registered and integrated, including using the Segment Editor module in 3D Slicer to extract tissue images in the circular field of view of the continuous scanning image; based on the extracted tissue images of the continuous slices, 3D Slicer is used to construct a 3D Mesh of the outer contour of the tissue, and it is smoothed, and the final 3DMesh is stored in obj format; based on the first 40 principal components, the Louvain algorithm is used to cluster the single-cell transcriptome data (resolution = 1) to identify the cell type characteristics of the tissue; the FindTransferAnchors and TransferData functions in Seurat are used to map the Stereo-seq data to the single-cell data, and the cell type is determined according to the mapping score of each Squarebin of the Stereo-seq data; according to the slice position in the Stereo-seq continuous slice experiment record, the Stereo-seq corresponding 3D Mesh; use the TrakEM2 elastic registration algorithm in ImageJ to align the Stereo-seq continuous sections with the X-ray images at the corresponding Z-axis position to determine the spatial position of the Stereo-seq continuous sections in the 3D Mesh; finally, the information on the Stereo-seq continuous sections, such as gene expression and tissue type information, is displayed at the accurate spatial position within the tissue 3D Mesh, thereby obtaining a three-dimensional reconstruction of the spatial transcriptome of single cells in the plant root tip.

10. A dehydration and drying method suitable for X-ray imaging of plant root tips (e.g., soybean root tips), characterized in that: The method comprises the following steps: (1) Fix the plant root tip by vacuum infiltration with 50% formaldehyde-acetic acid-ethanol (FAA) until no bubbles emerge from the sample (-0.08 MPa, 4°C); (2) Dehydrate in a centrifuge tube using gradient ethanol: 50% ethanol → 70% ethanol → 80% ethanol → 90% ethanol → 95% ethanol → 100% ethanol → 100% ethanol → 100% ethanol, with each gradient lasting 15 min. Place on ice. (3) Pour out the 100% ethanol and replace it with ethanol:acetone in a volume ratio of 3:1 for 30 minutes, place on ice; replace it with ethanol:acetone in a volume ratio of 3:2 for 30 minutes, place on ice; replace it with acetone for 30 minutes; repeat the acetone replacement for 30 minutes; (4) The plant root tip is placed in a basket and placed in a critical point drying instrument, liquid CO2 is replaced, the temperature is raised to 31.1°C and the gas is slowly exhausted to complete critical point drying, and the dehydrated plant root tip is obtained.

Images