A method and system for point-to-point matching of spatial transcriptomics and spatial metabolomics

Abstract

The present invention discloses a method for point-to-point matching of spatial transcriptomics and spatial metabolomics. By matching the spatial information of spatial transcriptomics and spatial metabolomics and correlating the data, point-to-point matching of spatial transcriptomics and spatial metabolomics is achieved. The present invention obtains the spatial coordinate position information of the identification points on the spatial metabolomics through registration; converts the spatial information of spatial transcriptomics and spatial metabolomics into a unified spatial information identifier; calculates the final scaling ratio and the final rotation angle between spatial transcriptomics and spatial metabolomics; uses the transformed spatial identifier method to convert the spatial coordinates of the spatial metabolomics so that the transformed spatial metabolomics coordinates match the spatial transcriptomics coordinates; fits the data of the spatial metabolomics and correlates it with the spots of spatial transcriptomics; sums up the underlying data of pixel points to obtain the spatial metabolomics data corresponding to each spot of spatial transcriptomics. The present invention also discloses a point-to-point matching system for implementing the above method.

Description

Technical Field

[0001] The present invention belongs to the technical field of image data processing, and relates to a method and system for point-to-point matching of spatial transcriptome and spatial metabolome. Background Art

[0002] Spatial transcriptome sequencing is the in-depth development result of single-cell transcriptome technology. It can simultaneously obtain the spatial position information and gene expression data of cells without the need to prepare cell suspensions, further promoting the research on the true gene expression of in-situ cells in tissues, and providing an important research means for multiple fields such as tissue cell function, microenvironment interaction, developmental process lineage tracing, and disease pathology.

[0003] As an emerging and cutting-edge technology that follows the development of high-throughput single-cell transcriptome technology, spatial transcriptome is a technology that can retain tissue spatial position and simultaneously analyze the transcriptome information of tissue sections. Spatial transcriptome is a technology for analyzing RNA-seq data at the spatial level to analyze all mRNAs in a single tissue section. Spatial barcode reverse transcription primers are orderly attached to the chip surface, making it possible to encode and obtain spatial position information during sample processing and subsequent sequencing. When a tissue cryosection is attached to the spatial transcriptome chip, the barcode primers bind and capture adjacent mRNAs from the tissue. The captured mRNAs start reverse transcription, and the resulting cDNA contains spatial barcodes. By analyzing the sequences of spatial barcodes in the sequencing results, the sequences transcribed by each mRNA can be mapped back to the starting positions in the tissue section.

[0004] Spatial metabolomics is a type of mass spectrometry imaging technology that can directly obtain the structural, content, and spatial distribution information of a large number of known or unknown endogenous metabolites, exogenous drugs, and other molecules from biological tissues. It has the outstanding advantages of high specificity, high throughput, and spatial information retention without the need for chemical or radioactive labeling and complex sample pretreatment.

[0005] Spatial metabolomics uses the method of combining mass spectrometry imaging and metabolomics, making full use of the ability of mass spectrometry imaging to accurately identify and locate the differential distribution of multiple metabolites between tissues and even cells, and combining the ability of metabolomics to perform in-depth metabolomics analysis on target micro-region tissues and obtain metabolite types and contents, realizing the detection of the spatial distribution of metabolites in biological samples. Spatial metabolomics can directly scan and analyze biological tissue sections under label-free conditions, and perform spatial position localization through the scanning information of scanning points.

[0006] Transcriptome is an important method to obtain gene expression in an organism, and metabolome is the basis and direct manifestation of the biological phenotype. Metabolites are the ultimate result of gene transcription under internal and external regulation in an organism and are the material basis of the biological phenotype. In the era of systems biology research, the biological development process is complex and variable, and the gene regulatory network is complex. Using only one type of omics for systems biology research is often one-sided. By performing correlation analysis between spatial transcriptome and spatial metabolome, analyzing the internal changes in an organism from two levels of cause and result, locking the key pathways related to metabolite changes, constructing a core regulatory network, comprehensively exploring the growth, development and stress mechanisms of organisms, and explaining biological problems as a whole.

[0007] However, due to the different spatial information identifiers of spatial transcriptome and spatial metabolome, different experimental operation steps for different sliced samples and non-identical sliced samples, and different spatial distributions, it is very difficult to perform spatial position registration and point-to-point data matching for the data of the two different systems, and it is even more difficult to perform correlation analysis between spatial transcriptome and spatial metabolome.

[0008] Specifically, due to the differences in detection platforms and experimental operations between spatial transcriptome and spatial metabolome, the spatial distributions of the two sets are different. The spatial transcriptome uses a chip as the detection platform. The detection area of the chip is 6.5 mm × 6.5 mm, with spot as the smallest detection unit. The diameter of a single spot is 55 μm, and the distance between the centers of spots is 100 μm. The upper and lower layers are arranged in a staggered manner. The spatial metabolome uses a mass spectrometer as the detection platform, and its detection area is not restricted, up to 10 cm × 10 cm at most, with pixel as the smallest detection unit. The common size of a single pixel is 100 μm × 100 μm, and the pixels are distributed in a square combination. The different spatial distributions of spatial transcriptome and spatial metabolome make it difficult to correlate spots with pixels, and it is even more difficult to correlate the underlying data within spots and pixels. Summary of the Invention

[0009] In order to solve the deficiencies of the existing technology, the purpose of the present invention is to provide a point-to-point matching method and system for spatial transcriptome and spatial metabolome.

[0010] The present invention proposes a point-to-point matching method for spatial transcriptome and spatial metabolome, which realizes the point-to-point matching of spatial transcriptome and spatial metabolome by matching the spatial information of spatial transcriptome and spatial metabolome and correlating the data.

[0011] It includes the following steps:

[0012] Step 1: Register the HE staining map containing spatial transcriptome information with marked positions with the spatial metabolome imaging map to obtain the spatial coordinate position information of the marked positions on the spatial metabolome.

[0013] The identification points are all located within the sample detection range of the spatial transcriptome, and the number of the identification points is ≥6; the identification points contain barcode information, which represents the spatial information of the identification points.

[0014] After the HE staining image and the spatial metabolomics imaging map are registered, the HE staining image and the spatial metabolomics imaging map are matched to obtain the spatial coordinate information of the identification point on the spatial metabolomics imaging map.

[0015] Step 2: Convert the spatial information of the spatial transcriptome and the spatial metabolome into a unified spatial information identifier;

[0016] The spatial coordinate systems of the spatial transcriptome and the spatial metabolome are unified by respectively performing operations on the two sets of spatial numbers x and y in the spatial information corresponding to the barcode in the spatial transcriptome and the two sets of spatial numbers x and y in the spatial information of the spatial metabolome and converting them into a micron coordinate system.

[0017] Step 3: Calculate the final scaling ratio and final rotation angle between the spatial transcriptome and the spatial metabolome;

[0018] The scaling ratios between each pair of landmark points were calculated, and the obtained scaling ratio data were screened to eliminate the data that did not meet the requirements. The remaining scaling ratio data were averaged to obtain the final scaling ratio for spatial metabolome conversion.

[0019] The rotation angle between the line connecting any two identification points in the spatial transcriptome and the line connecting two corresponding identification points in the spatial metabolome was calculated; the rotation angles of all the lines connecting any two points were calculated according to the principle of permutation and combination, and the average of all the obtained rotation angles was calculated to obtain the final rotation angle.

[0020] Step 4: Using the final rotation angle, final scaling ratio, and spatial transcriptome center identification point coordinates, the spatial metabolome coordinates are transformed using the transformation spatial identification method so that the transformed spatial metabolome coordinates match the spatial transcriptome coordinates.

[0021] Step 5: Fit the spatial metabolome data and associate them with the spatial transcriptome spots;

[0022] After amplifying the pixels in the spatial metabolome, the pixels are assigned according to the distance between the center position of the pixel after fitting the spatial metabolome and the center position of the spot in the spatial transcriptome.

[0023] Step 6: Add the underlying pixel data corresponding to each spot to obtain the spatial metabolome data corresponding to each spot in the spatial transcriptome.

[0024] Sum the underlying data of multiple pixels assigned to the same spot to obtain the spatial metabolome data corresponding to each spot in the spatial transcriptome.

[0025] The present invention also proposes a hardware system for implementing the above method. The hardware system includes: a memory and a processor; a computer program is stored on the memory, and when the computer program is executed by the processor, the above method is implemented.

[0026] The present invention also proposes a system for implementing the above method for point-to-point matching of spatial transcriptome and spatial metabolome, and the application of the above point-to-point matching method in the correlation analysis of spatial transcriptome and spatial metabolome.

[0027] The point-to-point matching system for spatial transcriptome and spatial metabolome includes a spatial information acquisition module, a spatial coordinate unification module, a spatial parameter calculation module, a spatial information conversion module, a spatial metabolome data fitting module, and a spatial transcriptome and spatial metabolome data association module;

[0028] The spatial information acquisition module registers the HE staining map containing spatial transcriptome information with marked positions with the spatial metabolome imaging map to obtain the spatial coordinate position information of the marked positions on the spatial metabolome.

[0029] The spatial coordinate unification module converts the spatial information of the spatial transcriptome and the spatial metabolome into a unified spatial information identifier.

[0030] The spatial parameter calculation module calculates the final scaling ratio and the final rotation angle between the spatial transcriptome and the spatial metabolome.

[0031] The spatial information conversion module uses the three parameters of the final rotation angle, the final scaling ratio, and the coordinates of the central identification point of the spatial transcriptome to convert the spatial coordinates of the spatial metabolome using the conversion spatial identification method, so that the converted spatial metabolome coordinates match the spatial transcriptome coordinates.

[0032] The spatial metabolome data fitting module fits the data of the spatial metabolome and associates it with the spots of the spatial transcriptome.

[0033] The spatial transcriptome and spatial metabolome data association module performs a summation operation on the underlying data of the pixels corresponding to each spot to obtain the spatial metabolome data corresponding to each spot in the spatial transcriptome.

[0034] In the point-to-point matching method, the spatial information matching is carried out through the spatial information matching method of spatial transcriptomics and spatial metabolomics. The spatial information matching method unifies the spatial information identifiers of spatial transcriptomics and spatial metabolomics, and converts the spatial coordinate system matching of spatial metabolomics into the spatial coordinates of spatial transcriptomics to achieve the spatial information matching of spatial transcriptomics and spatial metabolomics, including the following steps:

[0035] Step (1): Select 6 or more points (spots) evenly distributed (selected around and in the middle of the entire sample detection range to extend the selected points to the entire sample detection range) within the sample detection range of spatial transcriptomics as identification points, obtain the barcode information corresponding to the identification points (spots), and mark the selected points (spots) on the corresponding HE staining map;

[0036] The HE staining is one of the most basic and widely used technical methods in histology, pathology teaching and research. The pathological features presented by HE staining are associated with spatial transcriptomics.

[0037] There are a total of 4 spatial transcriptome chips on the expression slide of the spatial transcriptomics; the detection area size of each spatial transcriptome chip is 6.5mm×6.5mm, with a total of 4992 points (spots), the diameter of a single spot is 55μm, and the distance between the centers of every two adjacent spots (spots) is 100μm; the spot is the smallest unit detected in spatial transcriptomics.

[0038] The barcode is the spatial barcode in the Visium spatial transcriptomics technology. Each point (spot) has its unique barcode encoding, and the spatial position information of the corresponding point (spot) can be obtained through the spatial barcode.

[0039] The sample after HE staining can still be used for spatial transcriptomics detection, and it will not affect the spatial transcriptomics data. The HE staining map can be matched with the spatial position of the spatial transcriptomics data, and spots can be marked on the HE staining map.

[0040] Step (2): Register the HE staining map with the spatial position marked in step one with the spatial metabolomics imaging map to obtain the spatial coordinate position information of the corresponding pixel points of the marked points on the spatial metabolomics.

[0041] The pixel point is the smallest unit detected in spatial metabolomics; the resolution of the smallest unit detected in spatial metabolomics varies according to the mass spectrometry scanning rate and the moving platform rate. The greater the rate, the greater the scanning resolution.

[0042] The resolution of the smallest unit detected in the spatial metabolome represents the distance between pixel points; different resolutions result in different distances between pixel points; the conversion of spatial information in the spatial metabolome is affected by the resolution size.

[0043] The registration operation refers to using the spatial metabolome imaging map as a reference, rotating, scaling, and flipping the HE staining image through the MSIReader software to match the spatial metabolome imaging map and the HE staining map, and obtaining the spatial coordinate information of the identification sites on the HE staining map in the spatial metabolome imaging map through the software.

[0044] Step (3): Convert the barcode spatial information of the spatial transcriptome and the spatial information of the spatial metabolome into a unified spatial information identifier.

[0045] The conversion into a unified spatial information identifier means performing operations on the two groups of spatial numbers x and y in the spatial information corresponding to the barcode in the spatial transcriptome and the two groups of spatial numbers x and y in the spatial information of the spatial metabolome respectively, and obtaining a unified spatial coordinate system with a unified standard (i.e., converting the coordinates of both the spatial transcriptome and the spatial metabolome into a coordinate system in micrometers).

[0046] The conversion formula for the x spatial number in the spatial transcriptome is as follows:

[0047] UTX = (TransX / 2 + 0.5) * transresolution;

[0048] The conversion formula for the y spatial number in the spatial transcriptome is as follows:

[0049] UTY = TransY * sqrt(0.75) * transresolution;

[0050] Among them, UTX and UTY are the spatial coordinates after unifying the standards of the spatial transcriptome data; TransX and TransY are the two groups of spatial numbers x and y in the spatial transcriptome respectively; transresolution is the numerical value of the spatial transcriptome resolution; sqrt is the square root operation.

[0051] The conversion formula for the x spatial number in the spatial metabolome is as follows:

[0052] UMX = MetaX * metaresolution;

[0053] The conversion formula for the y spatial number in the spatial metabolome is as follows:

[0054] UMY = MetaY * metaresolution;

[0055] Among them, UMX and UMY are the spatial coordinates after unifying the standards of spatial metabolome data; MetaX and MetaY are two groups of spatial numbers of x and y in the spatial metabolome; and metaresolution is the numerical value of the spatial metabolome resolution.

[0056] Step (4): Calculate the distances between different identification points in the spatial transcriptome; calculate the distances between different identification points in the spatial metabolome; and calculate the scaling ratio of the distances between the corresponding identification points of the spatial transcriptome and the spatial metabolome.

[0057] In order to evaluate the accuracy of the correspondence of the identification points in the spatial transcriptome and the spatial metabolome and correct the position differences of the marker points caused by experimental instrument errors, the calculation of the scaling ratio is carried out.

[0058] The calculation formula of the scaling ratio is as follows:

[0059] Ratio=sqrt((UTXa - UTXb) 2 +(UTYa - UTYb) 2 ) / sqrt((UMXa - UMXb) 2 +(UMYa - UMYb) 2 )

[0060] Among them, UTXa, UTYa, UTXb, and UTYb are the X and Y coordinate values of marker points a and b in the spatial transcriptome after unifying the spatial coordinates; UMXa, UMYa, UMXb, and UMYb are the X and Y coordinate values of marker points a and b in the spatial metabolome after unifying the spatial coordinates; and Ratio is the scaling ratio of the distance between marker points a and b in the spatial transcriptome to the distance between marker points a and b in the spatial metabolome.

[0061] After obtaining the scaling ratios between each pair of marker points, it is also necessary to screen the scaling ratio data. The screening operation includes: removing the data with scaling ratio results greater than 1.1 and less than 0.91, and at the same time ensuring that the number of remaining scaling ratio data ≥ 4. Calculate the mean of the remaining scaling ratio data to obtain the final scaling ratio for spatial metabolome conversion; if the number of remaining scaling ratio data < 4, then go back to step (1) and start over.

[0062] Step (5): Judge whether there is a flip during the sample detection of the spatial transcriptome and the spatial metabolome by the rotation angle of the pairwise connection lines of each identification point position in the spatial transcriptome and the spatial metabolome, and calculate the final rotation angle between the detected samples of the spatial transcriptome and the spatial metabolome.

[0063] The marked point positions are the marked point positions after being screened by the scaling ratio; if the deviation of all rotation angles between the connecting lines of each pair of marked point positions is within 5°, it indicates that the sample has not been flipped; if the deviation of all rotation angles between the connecting lines of each pair of marked point positions is outside 5°, modify the conversion formula of the y-space number in the spatial metabolome in step (3) to:

[0064] UMY = -MetaY * resolution,

[0065] Then calculate the rotation angles between each pair of marked point positions in the spatial transcriptome and spatial metabolome detection samples. After obtaining all the rotation angles with a deviation within 5°, calculate the average value of all the rotation angles to obtain the final rotation angle;

[0066] The calculation formula of the rotation angle is as follows:

[0067] Angle = arctan((UMYa - UMYb) / (UMXa - UMXb)) - arctan((UTYa - UTYb) / (UTXa - UTXb));

[0068] Among them, UTXa, UTYa, UTXb, and UTYb are the X and Y coordinate values of the marked points a and b in the spatial transcriptome after unified spatial coordinates; UMXa, UMYa, UMXb, and UMYb are the X and Y coordinate values of the marked points a and b in the spatial metabolome after unified spatial coordinates; arctan is the calculation of the arctangent function; Angle is the rotation angle between the connecting line of the marked points a and b in the spatial transcriptome and the connecting line of the marked points a and b in the spatial metabolome.

[0069] Step (6): Use the conversion spatial identification method to convert the spatial coordinates of the spatial metabolome through three parameters: the final rotation angle, the final scaling ratio, and the coordinates of the central identification point of the spatial transcriptome, so that the converted spatial metabolome coordinates match the spatial transcriptome coordinates.

[0070] The central identification point of the spatial transcriptome refers to the position of the center point of the spatial transcriptome detection chip;

[0071] The conversion spatial identification method refers to converting the spatial position of each pixel point in the spatial metabolome into the corresponding coordinates in the spatial transcriptome through spatial coordinate conversion. The final scaling ratio and the final rotation angle obtained in steps (4) and (5) also satisfy the following calculation formula:

[0072] Ratio’ = sqrt((UMX’ - UTXo) 2 + (UTY’ - UTYo) 2 ) / sqrt((UMX - UMXo) 2 + (UMY - UMYo) 2);

[0073] Angle’ = arctan((UMY - UMYo) / (UMX - UMXo)) - arctan((UMY’ - UTYo) / (UMX’ - UTXo));

[0074] Where Ratio’ is the final scaling ratio; Angle’ is the final rotation angle; UTXo and UTYo are the spatial information after unifying the coordinate system of the central point position of the spatial transcriptome detection chip; UMXo and UMYo are the spatial information after unifying the coordinate system of the corresponding position of the central point position of the spatial transcriptome detection chip in the spatial metabolome; UMX and UMY are the X and Y coordinate values after unifying the spatial coordinates of the spatial metabolome; UMX’ and UMY’ are the X and Y coordinate values after the spatial metabolome matches the spatial transcriptome.

[0075] Through the obtained final rotation angle, final scaling ratio, and the coordinate parameters of the central identification point of the spatial transcriptome, the X and Y coordinate values UMX’ and UMY’ after the spatial metabolome matches the spatial transcriptome can be obtained.

[0076] Since the spatial transcriptome and spatial metabolome analysis systems only recognize the corresponding spatial information patterns, in order for their spatial information to be mutually recognized, the unified and associated coordinate system is restored and transformed into the spatial information systems of the spatial transcriptome and spatial metabolome;

[0077] The calculation formula for converting the spatial information of the unified coordinate system into the spatial information system of the spatial transcriptome is as follows:

[0078] UMX’ = (TransX’ / 2 + 0.5) * transresolution;

[0079] UMY’ = TransY’ * sqrt(0.75) * transresolution;

[0080] Where UMX’ and UMY’ are the spatial information after the spatial metabolome matches the spatial transcriptome respectively; transresolution is the numerical value of the spatial transcriptome resolution; sqrt is the square root operation; TransX’ and TransY’ are the two sets of spatial numbers of x and y in the spatial information of the spatial transcriptome after the spatial metabolome matches the spatial transcriptome spatial information.

[0081] The calculation formula for converting the spatial information of the unified coordinate system into the spatial information system of the spatial metabolome is as follows:

[0082] UMX’ = MetaX’ * metaresolution;

[0083] UMY’ = MetaY’ * metaresolution;

[0084] Among them, UMX’ and UMY’ are the spatial information after the spatial metabolome matches the spatial transcriptome; metaresolution is the numerical value of the spatial metabolome resolution; MetaX’ and MetaY’ are two sets of spatial numbers of x and y in the spatial metabolome spatial information after the spatial metabolome matches the spatial information of the spatial transcriptome and is transformed into the spatial metabolome spatial information.

[0085] The present invention also proposes a system for implementing the above-mentioned method for matching the spatial information of the spatial transcriptome and the spatial metabolome, and the application of the above-mentioned method for matching spatial information in the matching of the spatial information of the spatial transcriptome and the spatial metabolome.

[0086] The system for matching the spatial information of the spatial transcriptome and the spatial metabolome includes a spatial information acquisition module, a spatial coordinate unification module, a spatial parameter operation module, and a spatial information transformation module;

[0087] The spatial information acquisition module registers the HE staining map containing the spatial transcriptome information with the identification points with the spatial metabolome imaging map to obtain the spatial coordinate position information of the identification points on the spatial metabolome.

[0088] The spatial coordinate unification module transforms the spatial information of the spatial transcriptome and the spatial metabolome into a unified spatial information identifier.

[0089] The spatial parameter operation module calculates the final scaling ratio and the final rotation angle between the spatial transcriptome and the spatial metabolome.

[0090] The spatial information transformation module uses the three parameters of the final rotation angle, the final scaling ratio, and the coordinates of the central identification point of the spatial transcriptome to convert the spatial coordinates of the spatial metabolome using the conversion spatial identifier method so that the converted spatial metabolome coordinates match the spatial transcriptome coordinates.

[0091] Data association in the point-to-point matching method refers to integrating the spatial distribution pattern of the spatial transcriptome by fitting the spatial metabolome data, and finally realizing data association between the spatial metabolome and the spatial transcriptome at the point-to-point level, including the following steps:

[0092] Step I: Fit the data of the spatial metabolome to improve the pixel resolution of the spatial metabolome;

[0093] The fitting refers to amplifying the pixel points in the spatial metabolome as needed, and amplifying them according to different fitting methods according to whether the original pixel points are located in the sample area and / or the non-sample area.

[0094] If there are only two pixel points and both are located in the sample area, the calculation method of the intermediate pixel point after amplification of the two pixel points is as follows:

[0095] Calculate the underlying data of the amplified pixel points between the two pixel points according to the following formula:

[0096] MZ1ab = (MZ1a + MZ1b) / 2;

[0097] MZ2ab = (MZ2a + MZ2b) / 2;

[0098] …

[0099] MZnab = (MZna + MZnb) / 2;

[0100] Among them, pixel points a and b are adjacent pixel points; MZ1a, MZ2a, …, MZna are the expression intensities of the MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to pixel point a respectively; MZ1b, MZ2b, …, MZnb are the expression intensities of the MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to pixel point b respectively; MZ1ab, MZ2ab, …, MZnab are the expression intensities of the MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to the amplified pixel points between pixel points a and b respectively; the amplified pixel points between pixel points a and b are abbreviated as {ab}.

[0101] If there are only two pixel points, one pixel point is a sample area pixel point, and the other pixel point is a non-sample area pixel point, the calculation method of the intermediate pixel point after amplification of the two pixel points is as follows:

[0102] Calculate the underlying data of the amplified pixel points between the two pixel points according to the following formula:

[0103] MZ1ab = 0;

[0104] MZ2ab = 0;

[0105] …

[0106] MZnab = 0;

[0107] Among them, pixel points a and b are adjacent pixel points; MZ1ab, MZ2ab, …, MZnab are the expression intensities of the MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to the amplified pixel points between pixel points a and b respectively; the amplified pixel points between pixel points a and b are abbreviated as 0, indicating a non-sample area pixel point.

[0108] If there are only two pixel points and both pixel points are non-sample area pixel points, the calculation method of the intermediate pixel point after amplification of the two pixel points is as follows:

[0109] Calculate the underlying data of the amplified pixels between two pixel points according to the following formula:

[0110] MZ1ab = 0;

[0111] MZ2ab = 0;

[0112] …

[0113] MZnab = 0;

[0114] Wherein, pixel points a and b are adjacent pixel points; MZ1ab, MZ2ab, …, MZnab are the expression intensities of the mass-to-charge ratios MZ1, MZ2, …, MZn corresponding to the amplified pixel points between pixel points a and b respectively; the amplified pixel points between pixel points a and b are abbreviated as 0, indicating that the amplified pixel points are non-sample area pixel points.

[0115] Step II, associate the pixel points after fitting the spatial metabolome with the spots of the spatial transcriptome;

[0116] The association means corresponding the pixel points after fitting the spatial metabolome with the spots of the spatial transcriptome; the attribution of the pixel points is carried out by calculating the distance between the central position of the pixel points after fitting the spatial metabolome and the central position of the spots of the spatial transcriptome; if the distance between the central position of the pixel points after fitting the spatial metabolome and the central position of a certain spot of the spatial transcriptome is less than the diameter of the spot of the spatial transcriptome, the pixel points after fitting the spatial metabolome correspond to the spot in the spatial transcriptome.

[0117] Step III, perform a summation operation on the underlying data of the pixel points corresponding to each spot;

[0118] The summation operation means integrating the underlying data of multiple pixel points attributed to the same spot into the underlying data of one spot, and calculating through the following formula:

[0119] MZ1spotA = (MZ1a + MZ1b + … + MZ1x) / j;

[0120] MZ2spotA = (MZ2a + MZ2b + … + MZ2x) / j;

[0121] …

[0122] MZnspotA = (MZna + MZnb + … + MZnx) / j;

[0123] Among them, MZ1a, MZ2a, …, MZna are the expression intensities of MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to the a pixel points associated with Aspot, respectively; MZ1b, MZ2b, …, MZnb are the expression intensities of MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to the b pixel points associated with A spot, respectively; MZ1x, MZ2x, …, MZnx are the expression intensities of MZ1, MZ2, …, MZn mass-to-charge ratios corresponding to the x pixel points associated with Aspot, respectively; j is the number of pixel points associated with A spot; MZ1spotA, MZ2spotA, …, MZnspotA are the underlying data after the spatial metabolome addition corresponding to A spot, respectively.

[0124] The underlying data refers to the expression intensity data of mz corresponding to each pixel point in the spatial metabolome detection. mz is the ratio of the mass number to the charge number of a charged particle, which is the unique data information of a substance, and substances are usually referred to by mz.

[0125] Step IV: Obtain the spatial metabolome data corresponding to each point spot of the spatial transcriptome.

[0126] Process the spatial metabolome data according to the spatial number combinations of x and y for coordinate positions, and there is a corresponding expression intensity under each mz at the new spatial coordinate positions. The expression intensity is the relative expression value of the spatial metabolite corresponding to each pixel point for mz, and the numerical size represents the relative content level. The larger the value, the higher the relative content of the corresponding pixel point.

[0127] The spatial transcriptome data corresponds one-to-one with the above-mentioned spatial metabolite coordinates by Barcode encoding, and includes the count value of each transcription gene corresponding to the coordinates corresponding to the Barcode encoding. The gene count value is the transcription quantity of the corresponding transcription gene detected in the spatial transcriptome, and the transcriptional expression is judged by the size of the count value. The larger the count value, the higher the expression of the corresponding transcriptome.

[0128] The present invention also proposes a system for implementing the above-mentioned method for associating spatial transcriptome and spatial metabolome data, and the application of the above-mentioned data association method in the association of spatial transcriptome and spatial metabolome data.

[0129] The system for associating spatial transcriptome and spatial metabolome data includes a spatial metabolism data fitting module and a spatial transcription and spatial metabolism data association module;

[0130] The spatial metabolism data fitting module fits the data of the spatial metabolome and associates it with the point spot of the spatial transcriptome;

[0131] The spatial transcriptomics and spatial metabolomics data association module sums up the underlying data of the pixel points corresponding to each spot, and obtains the spatial metabolomics data corresponding to each spot in the spatial transcriptomics.

[0132] The beneficial effects of the present invention include: through a method and system for point-to-point matching of spatial transcriptomics and spatial metabolomics, the present invention realizes the point-to-point matching of spatial transcriptomics and spatial metabolomics, reduces the selection of manual regional matching of spatial transcriptomics and spatial metabolomics, and the amount of associated data after point-to-point matching is greatly increased, providing more reliable data support for subsequent association analysis of spatial transcriptomics and spatial metabolomics. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Figure 1 It shows a schematic flow chart of the method for point-to-point matching of spatial transcriptomics and spatial metabolomics of the present invention.

[0134] Figure 2 It shows a schematic diagram of the detection method for spatial transcriptomics chips.

[0135] Figure 3 It shows a schematic diagram of the detection method for spatial metabolomics mass spectrometry.

[0136] Figure 4 It shows a schematic flow chart of the method for spatial information matching of spatial transcriptomics and spatial metabolomics.

[0137] Figure 5 It shows a schematic diagram of the detection direction and coordinate identification method for spatial metabolomics mass spectrometry.

[0138] Figure 6 It shows a schematic diagram of the spot distribution in the spatial transcriptomics and HE staining in Example 1 of the matching method.

[0139] Figure 7 It shows a schematic diagram of the identification of the selected spot positions in the spatial transcriptomics in Example 1 of the matching method.

[0140] Figure 8 It shows an imaging map of the spatial metabolomics of the unlabeled points in Example 1 of the matching method.

[0141] Figure 9 It shows a schematic diagram after the identification points are identified after matching the HE staining map of the spatial transcriptomics and the imaging map of the spatial metabolomics in Example 1 of the matching method.

[0142] Figure 10 It shows a mass spectrometry imaging map of converting the space of the spatial metabolomics into the space information of the spatial transcriptomics in Example 1 of the matching method.

[0143] Figure 11 It shows a schematic flow chart of the method for data association of spatial transcriptomics and spatial metabolomics.

[0144] Figure 12 Schematic diagram showing the spatial distribution method of spatial transcriptome detection.

[0145] Figure 13 Schematic diagram showing the spatial distribution and data storage method of spatial metabolome detection.

[0146] Figure 14 Schematic diagram showing the different superposition effect diagrams of the spatial distributions of spatial transcriptome and spatial metabolome.

[0147] Figure 15 Schematic diagram showing the operation display after two - time fitting when all 2×2 pixel points of the spatial metabolome are sample regions.

[0148] Figure 16 Schematic diagram showing the operation display after two - time fitting when 1 pixel point in the 2×2 pixel points of the spatial metabolome is a non - sample region.

[0149] Figure 17 Schematic diagram showing the operation display after two - time fitting when 2 pixel points in the 2×2 pixel points of the spatial metabolome are non - sample regions.

[0150] Figure 18 Schematic diagram showing the operation display after two - time fitting when 3 pixel points in the 2×2 pixel points of the spatial metabolome are non - sample regions.

[0151] Figure 19 Schematic diagram showing the operation display after two - time fitting when all 2×2 pixel points of the spatial metabolome are non - sample regions.

[0152] Figure 20 Schematic diagram showing the association between the pixel points of the spatial metabolome and the spots of the spatial transcriptome.

[0153] Figure 21 Schematic diagram showing the result display of the sample region and non - sample region after two - time fitting.

[0154] Figure 22 Schematic diagram showing the association between the pixel points and spots of the sample region and non - sample region.

[0155] Figure 23 Schematic diagram showing the visualization diagram after the spatial metabolome data in Example 2 is transformed into the spatial transcriptome distribution.

[0156] Figure 24 Schematic diagram showing the change of the imaging diagram after the spatial metabolome data in Example 2 is fitted.

[0157] Figure 25 Schematic diagram showing the system for implementing the point - to - point matching method of the above - mentioned spatial transcriptome and spatial metabolome. Detailed implementation mode

[0158] In combination with the following specific embodiments and attached drawings, the present invention will be further described in detail. The processes, conditions, experimental methods, etc. for implementing the present invention, except for the specifically mentioned content below, are all common knowledge and well-known common sense in the art, and the present invention has no particularly restricted content.

[0159] The present invention provides a method and system for point-to-point matching of spatial transcriptomics and spatial metabolomics.

[0160] The method for point-to-point matching of spatial transcriptomics and spatial metabolomics proposed by the present invention includes a spatial information matching method and a data association method between spatial transcriptomics and spatial metabolomics.

[0161] Among them, the spatial information matching method is used for the conversion of spatial coordinate position information in spatial transcriptomics and spatial metabolomics, so that the spatial coordinate positions of two different omics systems can be matched. The data association method is used for the underlying data processing in spatial transcriptomics and spatial metabolomics, so that the data of two different omics systems can be associated.

[0162] Specifically, the spatial information matching method between spatial transcriptomics and spatial metabolomics solves the problem of spatial position mismatch caused by different spatial information identifiers, different experimental operation steps for different slice samples, and non-identical slice samples between spatial transcriptomics and spatial metabolomics.

[0163] The data association method between spatial transcriptomics and spatial metabolomics solves the problem that due to the staggered distribution of the upper and lower layers of spatial transcriptomics and the square combination distribution of spatial metabolomics, it is impossible to spatially associate data with different spatial distributions.

[0164] Combining the spatial information matching method and the data association method between spatial transcriptomics and spatial metabolomics and running the two methods together finally realizes the point-to-point matching of spatial transcriptomics and spatial metabolomics.

[0165] Specifically, the spatial information matching method refers to unifying the spatial information identifiers of spatial transcriptomics and spatial metabolomics, and converting the spatial coordinate system of spatial metabolomics to match the spatial coordinates of spatial transcriptomics, so as to realize the spatial information matching between spatial transcriptomics and spatial metabolomics.

[0166] As Figure 4 shown, it includes the following steps:

[0167] (1) Select 6 or more spots as identification points within the sample detection range of the spatial transcriptome, obtain the barcode spatial information corresponding to the identified spots, and mark the spots on the corresponding HE (hematoxylin-eosin) staining map. 6 is the minimum number of identification points. When the number of identification points is less than 6, the matching effect is poor, and the positions of the identification points are scattered throughout the chip detection area.

[0168] (2) Register the HE staining map with the identified points and the spatial metabolomics imaging map to obtain the spatial coordinate position information of the corresponding pixel points of the identified points on the spatial metabolomics. The detection resolution of the spatial metabolomics is usually 100μm, 40μm, 20μm, that is, the distance between pixel points is 100μm, 40μm, 20μm.

[0169] (3) Convert the barcode spatial information of the spatial transcriptome and the spatial metabolomics spatial information into a unified spatial information identifier.

[0170] (4) Calculate the distances between different identification points of the spatial transcriptome; calculate the distances between different identification points of the spatial metabolomics; and calculate the scaling ratio of the distances between the corresponding identification points of the spatial transcriptome and the spatial metabolomics. Since the spatial coordinate systems of the spatial transcriptome and the spatial metabolomics are unified, the theoretical scaling ratio is 1. However, since the samples of the spatial metabolomics and the spatial transcriptome are adjacent section samples, there are differences in actual sample detection. To prevent errors in point selection caused by different samples or scaling ratios not equal to 1 caused by instrument errors, scaling ratio correction is required in subsequent operations.

[0171] (5) Eliminate the scaling ratios greater than 1.1 and less than 0.91. After elimination, the remaining number of scaling ratios should be greater than or equal to 4. (If the requirements are not met, start from step (1) again), calculate the mean value of the remaining scaling ratios to obtain the final scaling ratio, which is used as the final scaling ratio between the spatial transcriptome and the spatial metabolomics. The final scaling ratio is used for point selection operations and data correction caused by experimental operation errors.

[0172] (6) Use the spatial information of the identification points that meet the scaling ratio conditions to determine whether there is a flip during the detection of the spatial transcriptome and spatial metabolome samples, and calculate the final rotation angle between the two detected samples. The final rotation angle is the average value of all the rotation angles obtained by calculating any two marker points, that is, calculate the rotation angle between two points in the spatial transcriptome and the corresponding two points in the spatial metabolome. Since at least 4 or more identification points are used for the calculation of the rotation angle, more than 6 combinations of rotation angles will be obtained finally. After calculating the rotation angles of all the identification point combinations of the spatial transcriptome and spatial metabolome, it is also necessary to determine whether the rotation angle deviation is ≤ 5°. If so, proceed to the subsequent average value calculation step; if not, return to step (3) and start over. The rotation angle deviation refers to the absolute difference between the rotation angle of each combination and the final rotation angle.

[0173] (7) Use the final rotation angle, final scaling ratio, and the coordinates of the central identification point of the spatial transcriptome, and use the method of converting spatial identification to convert the spatial coordinates of the spatial metabolome, so that the converted spatial metabolome coordinates finally match the spatial transcriptome coordinates.

[0174] The data association method refers to fitting the spatial metabolome data, integrating the fitted data into the spatial distribution pattern of the spatial transcriptome, and finally realizing the data association between the spatial metabolome and the spatial transcriptome at the point-to-point level.

[0175] As Figure 11 shown, it includes the following steps:

[0176] (1) Fit the data of the spatial metabolome twice. Through the fitting, the pixel point resolution of the spatial metabolome is increased from the original 100μm×100μm to 25μm×25μm;

[0177] (2) Associate the pixel points after fitting the spatial metabolome with the spots of the spatial transcriptome;

[0178] (3) Perform a summation operation on the underlying data of the pixel points corresponding to each spot;

[0179] (4) Obtain the spatial metabolome data corresponding to each spot of the spatial transcriptome.

[0180] Example 1

[0181] Matching method

[0182] (1) Select 6 spots as identification points within the sample detection range of the spatial transcriptome, obtain the barcode information corresponding to the identification spots, and mark the spots on the corresponding HE staining map (see Figure 6 and Figure 7 ). Figure 6Schematic diagram of spot distribution and HE staining in spatial transcriptomics; Figure 7 Distribution map of 6 spot positions selected in spatial transcriptomics.

[0183] Table 1 Barcode and spatial information of identification points in spatial transcriptomics

[0184] Barcode Identification point TransX TransY GGTAGTGCTCGCACCA a 72 34 b 29 19 GATGGCGCACACATTA c 13 51 d 112 14 CTGGACGCAGTCCGGC e 86 48 f 111 61

[0185] In Table 1, Barcode is the spatial barcode in spatial transcriptomics technology. Each spot has its unique barcode encoding, and the spatial position information of the corresponding spot can be obtained through the spatial barcode; TransX and TransY are two groups of spatial numbers of x and y in spatial transcriptomics;

[0186] (2) Use the HE staining map with identified positions to register with the spatial metabolomics imaging map to obtain the spatial coordinate position information of the corresponding pixel points of the identified positions on the spatial metabolomics (see CACAAGAAAGATATTA and ). AATAGAATCTGTTTCA Spatial metabolomics imaging map of unlabeled points; Schematic diagram including identification points after matching the spatial transcriptomics HE staining map and the spatial metabolomics imaging map.

[0187] Table 2 Spatial information table of identification points in spatial metabolomics

[0188]

[0189]

[0190] In Table 2, MetaX and MetaY are two groups of spatial numbers of x and y in spatial metabolomics.

[0191] (3) Convert the barcode spatial information of spatial transcriptomics and the spatial metabolomics spatial information into a unified spatial information identifier.

[0192] Table 3 Unified spatial information table

[0193] CTGCAAGCACGTTCCG Figure 8 Figure 9 Figure 8 a 3650 2944.5 4100 5200 b 1500 1645.5 5200 7500 c 700 4416.7 2200 8000 d 5650 1212.4 6200 3400 e 4350 4156.9 3000 4300 f 5600 5282.8 1900 2900

[0194] In Table 3, UTX and UTY are the spatial coordinates after unifying the spatial transcriptomics data standard; UMX and UMY are the spatial coordinates after unifying the spatial metabolomics data standard.

[0195] (4) Calculate the distances between different identification points in spatial transcriptomics; calculate the distances between different identification points in spatial metabolomics; and calculate the scaling ratio of the distances between the corresponding identification points of spatial transcriptomics and spatial metabolomics.

[0196] Table 4 Calculation Results Table of Scaling Ratios for Pairwise Identification Points

[0197]

[0198]

[0199] (5) Eliminate the scaling ratios greater than 1.1 and less than 0.91. After elimination, the remaining number of scaling ratios should be greater than or equal to 4. Calculate the mean of the remaining scaling ratios to obtain the final scaling ratio, which is used as the final scaling ratio for spatial transcriptomics and spatial metabolomics. The final scaling ratio is used for point selection operations, which are to eliminate points that introduce excessive errors and data correction due to experimental operation errors. The final scaling ratio is 1.03.

[0200] (6) Use the spatial information of the identification points that meet the scaling ratio conditions to determine whether there is a flip during the detection process of spatial transcriptomics and spatial metabolomics samples and calculate the final rotation angle between the two detected samples. The final rotation angle is 82.9°. The absolute difference between the rotation angle of each combination and the final rotation angle is called the rotation angle deviation. The final rotation angle is 82.9°, and the rotation angle of each combination and the final rotation angle are all ≤5°, all meeting the threshold of the rotation angle deviation.

[0201] Table 5 Calculation Results Table of Rotation Angles for Pairwise Identification Points

[0202] Figure 9 Identification point UTX 84.4 UTY 82.4 UMX 81.5 UMY 80.7 Pairwise identification points 83.6 Rotation angle 83.4 a - b 82.2 a - c 83.1 a - d 84.1 a - e 81.9 a - f 81.9 b - c 83.3 b - d 81.9 b - e 82.7 b - f 86.1 c - d 82.9

[0203] (7) Use the three parameters of the final rotation angle, the final scaling ratio, and the coordinates of the central identification point of spatial transcriptomics to convert the spatial coordinates of spatial metabolomics using the transformation spatial identification method, and finally make the converted spatial coordinates of spatial metabolomics match the spatial coordinates of spatial transcriptomics. As c - e shown, c - f is the mass spectrometry imaging map after the spatial metabolomics space is transformed into the spatial information of spatial transcriptomics.

[0204] Table 6 Table of Converting Partial Spatial Metabolomics Coordinates into Spatial Information of Spatial Transcriptomics

[0205] d - e d - f e - f Final rotation angle Figure 10 Figure 10 MetaX MetaY UMX UMY 1 1 100 100 8062.4 7392.4 80.6 73.9 160.2 85.4 2 1 200 100 8074.4 7296.5 80.7 73.0 160.5 84.3 3 1 300 100 8086.4 7200.5 80.8 72.0 160.7 83.1 4 1 400 100 8098.3 7104.6 81.0 71.0 161.0 82.0 5 1 500 100 8110.3 7008.7 81.1 70.1 161.2 80.9 6 1 600 100 8122.3 6912.8 81.2 69.1 161.4 79.8 7 1 700 100 8134.3 6816.9 81.3 68.2 161.7 78.7 8 1 800 100 8146.3 6721.0 81.5 67.2 161.9 77.6 9 1 900 100 8158.3 6625.0 81.6 66.2 162.2 76.5 10 1 1000 100 8170.3 6529.1 81.7 65.3 162.4 75.4 11 1 1100 100 8182.3 6433.2 81.8 64.3 162.6 74.3 12 1 1200 100 8194.2 6337.3 81.9 63.4 162.9 73.2 13 1 1300 100 8206.2 6241.4 82.1 62.4 163.1 72.1 14 1 1400 100 8218.3 6145.5 82.2 61.5 163.4 71.0 15 1 1500 100 8230.2 6049.5 82.3 60.5 163.6 69.9

[0206] In Table 6, MetaX and MetaY are two groups of spatial numbers of x and y in spatial metabolomics; UMX and UMY are the spatial coordinates after the spatial metabolomics data are unified; UMX’ and UMY’ are the spatial information after spatial metabolomics matches spatial transcriptomics; MetaX’ and MetaY’ are two groups of spatial numbers of x and y in the spatial information of spatial metabolomics after spatial metabolomics is transformed to match the spatial information of spatial transcriptomics; TransX’ and TransY’ are two groups of spatial numbers of x and y in the spatial information of spatial transcriptomics after spatial metabolomics is transformed to match the spatial information of spatial transcriptomics.

[0207] Example 2

[0208] Data association method

[0209] In the data association method, the following example is used to illustrate the improvement of the pixel resolution of spatial metabolomics by fitting:

[0210] As UMX’ shown, if the pixel points are arranged in a 2*2 pattern and all are pixel points in the sample area, the calculation method of the middle pixel point after amplification of the four pixel points is as follows:

[0211] Calculate the underlying data of the middle amplified pixel point of the 2*2 pixel points according to the following formula:

[0212] MZ1abcd = (MZ1a + MZ1b + MZ1c + MZ1d) / 4;

[0213] MZ2abcd = (MZ2a + MZ2b + MZ2c + MZ2d) / 4;

[0214] …

[0215] MZnabcd = (MZna + MZnb + MZnc + MZnd) / 4;

[0216] Among them, the pixel points a, b, c, and d are arranged in a 2*2 pattern; MZ1a, MZ2a,..., MZna are the expression intensities of the MZ1, MZ2,..., MZn mass-to-charge ratios corresponding to the pixel point a respectively; MZ1b, MZ2b,..., MZnb are the expression intensities of the MZ1, MZ2,..., MZn mass-to-charge ratios corresponding to the pixel point b respectively; MZ1c, MZ2c,..., MZnc are the expression intensities of the MZ1, MZ2,..., MZn mass-to-charge ratios corresponding to the pixel point c respectively; MZ1d, MZ2d,..., MZnd are the expression intensities of the MZ1, MZ2,..., MZn mass-to-charge ratios corresponding to the pixel point d respectively; MZ1abcd, MZ2abcd,..., MZnabcd are the expression intensities of the MZ1, MZ2,..., MZn mass-to-charge ratios corresponding to the middle pixel point after amplification between the four pixel points a, b, c, and d respectively; the pixel points amplified between the pixel points a, b, c, and d are abbreviated as {abcd}.

[0217] As UMY’ shown, if the pixel points are arranged in a 2*2 pattern and one of them is located in the non-sample area, the calculation method of the middle pixel point after amplification of the four pixel points is as follows:

[0218] Calculate the underlying data of the middle amplified pixel point of the 2*2 pixel points according to the following formula, where the pixel point a is the pixel point in the non-sample area:

[0219] MZ1abcd = (MZ1b + MZ1c + MZ1d) / 3;

[0220] MZ2abcd = (MZ2b + MZ2c + MZ2d) / 3;

[0221] …

[0222] MZnabcd = (MZnb + MZnc + MZnd) / 3;

[0223] Among them, pixel points a, b, c, and d are arranged in a 2*2 pattern; MZ1b, MZ2b, …, MZnb are the expression intensities of the mass-to-charge ratios of MZ1, MZ2, …, MZn corresponding to pixel point b respectively; MZ1c, MZ2c, …, MZnc are the expression intensities of the mass-to-charge ratios of MZ1, MZ2, …, MZn corresponding to pixel point c respectively; MZ1d, MZ2d, …, MZnd are the expression intensities of the mass-to-charge ratios of MZ1, MZ2, …, MZn corresponding to pixel point d respectively; MZ1abcd, MZ2abcd, …, MZnabcd are the expression intensities of the mass-to-charge ratios of MZ1, MZ2, …, MZn corresponding to the intermediate pixel points after amplification between the four pixel points a, b, c, and d respectively; the pixel points amplified between pixel points a, b, c, and d are abbreviated as {bcd}.

[0224] As MetaX‘ shown, if the pixel points are arranged in a 2*2 pattern and 2 pixel points are located in the non-sample area, the calculation method for the intermediate pixel points after amplification of the four pixel points is as follows:

[0225] Calculate the underlying data of the intermediate amplified pixel points of the 2*2 pixel points according to the following formula, where pixel points a and b are non-sample area pixel points:

[0226] MZ1abcd = (MZ1c + MZ1d) / 2;

[0227] MZ2abcd = (MZ2c + MZ2d) / 2;

[0228] …

[0229] MZnabcd = (MZnc + MZnd) / 2;

[0230] Among them, pixels a, b, c, and d are arranged in 2*2 order; MZ1c, MZ2c, …, MZnc are the expression intensities of the CPR of MZ1, MZ2, …, MZn corresponding to pixel c; MZ1d, MZ2d, …, MZnd are the expression intensities of the CPR of MZ1, MZ2, …, MZn corresponding to pixel d; MZ1abcd, MZ2abcd, …, MZnabcd are the expression intensities of the CPR of MZ1, MZ2, …, MZn corresponding to the amplified intermediate pixels between pixels a, b, c, and d; the amplified pixels between pixels a, b, c, and d are abbreviated as {cd}.

[0231] like MetaY’ As shown in the figure, if the pixels are arranged in a 2*2 pattern, and three of the pixels are located in the non-sample area, the calculation method for the middle pixel after the four pixels are amplified is as follows:

[0232] The underlying data of the amplified pixel in the middle of the 2*2 pixel point is calculated according to the following formula, where pixels a, b, and c are pixels in the non-sample area:

[0233] MZ1abcd=0;

[0234] MZ2abcd=0;

[0235] …

[0236] MZnabcd=0;

[0237] Pixels a, b, c, and d are arranged in a 2*2 pattern; MZ1abcd, MZ2abcd, …, MZnabcd are the expression intensities of the CNRs of the intermediate pixels between the four pixels a, b, c, and d after amplification, corresponding to MZ1, MZ2, …, MZn; the amplified pixels between the pixels a, b, c, and d are abbreviated as 0, indicating that the amplified pixels are non-sample area pixels.

[0238] like TransX’ As shown in the figure, if the pixels are arranged in a 2*2 pattern and are all in the non-sample area, the calculation method for the middle pixel after the four pixels are amplified is as follows:

[0239] The underlying data of the amplified pixel in the middle of the 2*2 pixel point is calculated according to the following formula:

[0240] MZ1abcd=0;

[0241] MZ2abcd=0;

[0242] …

[0243] MZnabcd=0;

[0244] Among them, pixel points a, b, c, and d are arranged in a 2*2 pattern; MZ1abcd, MZ2abcd, …, MZnabcd are the expression intensities of the mass-to-charge ratios of MZ1, MZ2, …, MZn corresponding to the intermediate pixel points after amplification between the four pixel points a, b, c, and d; the pixel points amplified between pixel points a, b, c, and d are abbreviated as 0, indicating that the amplified pixel points are non-sample area pixel points.

[0245] Example 3

[0246] Associate mouse kidney spatial metabolome data with spatial transcriptomics

[0247] (1) Fit the data of the mouse kidney spatial metabolome twice. After fitting, the pixel resolution of the spatial metabolome is increased from the original 100μm×100μm to 25μm×25μm; ( [[ID= )

[0248] (2) Associate the pixel points after fitting the empty metabolome with the spots of the spatial transcriptomics;

[0249] Table 7 Association table of partial spatial metabolome coordinates and spatial transcriptomics Barcode

[0250]

[0251]

[0252] In Table 7, Barcode is the spatial barcode in the spatial transcriptomics technology. Each spot has its unique barcode encoding, and the spatial position information of the corresponding spot can be obtained through the spatial barcode; the spatial metabolome coordinates are the combination of two sets of spatial numbers of x and y in the spatial metabolome.

[0253] (3) Perform summation operations on the underlying data of the pixel points corresponding to each spot ( ​ ); the underlying data refers to the expression intensity data of the corresponding mz within each pixel point in the spatial metabolome detection. mz is the ratio of the mass number to the charge number of a charged particle, which is unique data information of a substance, and usually mz is used to represent the substance.

[0254] (4) Obtain the spatial metabolome data corresponding to each spot of the spatial transcriptomics.

[0255] Table 8 Partial spatial metabolome data

[0256] ​ 1-5 2-4 3-5 5-5 7-5 71.01222 1400.171 2714.815 1905.272 1492.15 1368.167 72.71514 259.7251 515.8836 798.2261 715.2259 472.7763 72.99158 109.9754 1056.229 986.7844 1435.146 535.1709 73.02797 817.4667 0 631.1629 895.4172 454.4463 74.0232 27.17561 434.8098 135.878 63.1555 252.8567 75.00722 403.1064 787.5696 806.4386 759.8611 831.8674 75.45135 662.9783 756.3464 722.308 576.1749 635.5394 78.95747 9850.157 8887.678 10582.36 10793.19 10528.72 79.95577 1097.258 1293.686 1267.888 1538.529 1882.602 83.04869 523.8511 0 300.4212 590.7473 322.4308

[0257] In Table 8, m / z is the mass-to-charge ratio data in spatial metabolomics; 1-5, 2-4, 3-5, 5-5, 7-5 are the expression intensities corresponding to m / z under the spatial number combinations of x and y in spatial metabolomics. The expression intensity is the relative expression value of the spatial metabolite corresponding to m / z at each pixel point. The numerical size represents the relative content level. The larger the value, the higher the relative content of the corresponding pixel point.

[0258] Partial spatial transcriptome data in Table 9

[0259]

[0260]

[0261] In Table 9, the "Gene Name" column is the gene name in spatial transcriptomics; the values in the columns of GCCACCCATTCCACTT, TACTCACAACGTAGTA, GTTCGGTGTGGATTTA, GGAGACATTCACGGGC, TAGAACGCCAGTAACG are the count values of the corresponding genes encoded by the spatial transcriptome barcode.

[0262] The count value is the transcription quantity of the corresponding Gene Name detected in spatial transcriptomics. The transcriptional expression quantification is judged by the size of the count value. The larger the count value, the higher the expression of the corresponding transcriptome.

[0263] The protection scope of the present invention is not limited to the above embodiments. Without departing from the spirit and scope of the inventive concept of the present invention, the changes and advantages that can be conceived by those skilled in the art are included in the present invention, and the appended claims are taken as the protection scope.

Claims

1. A method for point-to-point matching of spatial transcriptome and spatial metabolome, characterized in that By matching the spatial information of the spatial transcriptome and the spatial metabolome and associating the data, point-to-point matching of the spatial transcriptome and the spatial metabolome is achieved; the method comprises the following steps: Step 1: align the HE staining image containing the spatial transcriptome information with the marked points with the spatial metabolome imaging map to obtain the spatial coordinate position information of the marked points on the spatial metabolome; Step 2: Convert the spatial information of the spatial transcriptome and the spatial metabolome into a unified spatial information identifier; Step 3: Calculate the final scaling ratio and final rotation angle between the spatial transcriptome and the spatial metabolome; Step 4: Using the final rotation angle, final scaling ratio, and spatial transcriptome center identification point coordinates, the spatial metabolome coordinates are transformed using the transformation spatial identification method so that the transformed spatial metabolome coordinates match the spatial transcriptome coordinates. Step 5: Fit the spatial metabolome data and associate them with the spatial transcriptome spots; Step 6: Add the underlying pixel data corresponding to each spot to obtain the spatial metabolome data corresponding to each spot in the spatial transcriptome.

2. The point-to-point matching method according to claim 1, characterized in that In step 1, the identification points are all located within the sample detection range of the spatial transcriptome, and the number of the identification points is ≥ 6; the identification points contain barcode information, which represents the spatial information of the identification points.

3. The point-to-point matching method according to claim 1, characterized in that, In step 1, after the HE staining image and the spatial metabolomics image are registered, the HE staining image and the spatial metabolomics image are matched to obtain the spatial coordinate information of the identification point on the spatial metabolomics image.

4. The point-to-point matching method according to claim 1, wherein In step 2, the spatial coordinate systems of the spatial transcriptome and the spatial metabolome are unified by respectively performing operations on the two sets of spatial numbers x and y in the spatial information corresponding to the barcode barcode in the spatial transcriptome and the two sets of spatial numbers x and y in the spatial metabolome spatial information and converting them into a micron coordinate system.

5. The point-to-point matching method according to claim 1, characterized in that In step 3, the scaling ratios between each pair of landmark points are calculated, and the obtained scaling ratio data are screened to eliminate the data that do not meet the requirements. The remaining scaling ratio data are averaged to obtain the final scaling for spatial metabolome conversion.

6. The point-to-point matching method according to claim 1, wherein In step three, the rotation angle between the line connecting any two identification points in the spatial transcriptome and the line connecting two corresponding identification points in the spatial metabolome is calculated; the rotation angle of all the lines connecting any two points is calculated according to the principle of permutation and combination, and the average of all the obtained rotation angles is calculated to obtain the final rotation angle.

7. The point-to-point matching method according to claim 1, wherein In step 5, after amplifying the pixels in the spatial metabolome, the pixels are assigned according to the distance between the center position of the pixel after fitting the spatial metabolome and the center position of the spot in the spatial transcriptome.

8. The point-to-point matching method according to claim 1, wherein In step 6, the underlying data of multiple pixels belonging to the same spot are summed to obtain the spatial metabolome data corresponding to each spot of the spatial transcriptome.

9. A system for implementing the point-to-point matching method according to any one of claims 1-8, characterized in that, The system includes a spatial information acquisition module, a spatial coordinate unification module, a spatial parameter calculation module, a spatial information conversion module, a spatial metabolic data fitting module, and a spatial transcription and spatial metabolic data association module; The spatial information acquisition module registers the HE staining map containing spatial transcriptome information with marked points with the spatial metabolomics imaging map to obtain the spatial coordinate position information of the marked points on the spatial metabolomics. The spatial coordinate unification module converts the spatial information of the spatial transcriptome and the spatial metabolomics into a unified spatial information identifier. The spatial parameter calculation module calculates the final scaling ratio and the final rotation angle between the spatial transcriptome and the spatial metabolomics. The spatial information conversion module uses the three parameters of the final rotation angle, the final scaling ratio, and the coordinates of the central identification point of the spatial transcriptome to convert the spatial coordinates of the spatial metabolomics using the conversion space identification method, so that the converted spatial metabolomics coordinates match the spatial transcriptome coordinates. The spatial metabolomics data fitting module fits the data of the spatial metabolomics and associates it with the spots of the spatial transcriptome. The spatial transcriptome and spatial metabolomics data association module performs a summation operation on the underlying pixel data corresponding to each spot to obtain the spatial metabolomics data corresponding to each spot of the spatial transcriptome.

10. A hardware system for implementing the method according to any one of claims 1-8, characterized in that, The hardware system includes: a memory and a processor; a computer program is stored on the memory, and when the computer program is executed by the processor, the method described in any one of claims 1-8 is implemented.

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