Imaging method, device, computer device and storage medium of brain tissue

Abstract

The application belongs to the field of mass spectrometry imaging, and provides a brain tissue imaging method and related equipment, which can improve the spatial resolution of the mass spectrum image of a brain tissue slice. The method comprises the following steps: determining the position parameter of a fine probe corresponding to a target brain tissue slice; controlling a target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate in the fine probe, so that the target brain tissue slice is desorbed and ionized; controlling the fine probe to scan the target brain tissue slice after desorption and ionization at a preset step length according to the position parameter, so as to obtain mass spectrum raw data corresponding to the target brain tissue slice; and determining a mass spectrum image corresponding to the target brain tissue slice according to the mass spectrum raw data corresponding to the target brain tissue slice.

Description

[Technical Field]

[0001] This application relates to the field of mass spectrometry imaging, and in particular to an imaging method, apparatus, computer device, and storage medium for brain tissue. [Background Technology]

[0002] The brain is one of the most complex organs in the human body. Unique anatomical regions within the brain are composed of a large number of different types of cells; these cells are regulated by complex neural networks that link these micro-regions to brain function.

[0003] Currently, many genomics and proteomics studies have revealed the regulatory mechanisms of the central nervous system (CNS) by mapping expression profiles. Metabolites, as the final products of molecular biology, include many small molecule biochemicals, such as neurotransmitters and complex lipids, which participate in signal transduction and regulation, and are of great significance to CNS function, the pathogenesis of mental illnesses, and drug development. However, most existing comprehensive brain metabolome maps are based on analyses of individual regions separated by manual dissection, resulting in low resolution and an inability to distinguish molecular differences between fine micro-regions. Furthermore, complex sample preprocessing processes can lead to the loss of some information. Therefore, there is a need to establish high-resolution in-situ molecular imaging methods for high-throughput characterization of metabolites in the brain.

[0004] As a representative technique in spatially resolved metabolomics analysis, mass spectrometry imaging (MSI) can perform non-targeted, label-free analysis of thousands of molecules (such as metabolites, lipids, peptides, proteins, and glycans) in complex surfaces, simultaneously obtaining comprehensive information on m / z, intensity, and localization in a single detection. In central nervous system research, MSI analyzes brain slices to create various molecular maps, revealing changes in the metabolome or lipidome under pathological conditions, showing great potential in exploring pathogenesis and discovering biomarkers. Spatial resolution is one of the important parameters, defined as the minimum pixel size required to generate a detectable signal. With continuous technological advancements, MSI with micrometer-level resolution has been able to precisely locate metabolites in complex brain tissue. However, it is well known that there is an inverse relationship between sensitivity and spatial resolution due to reduced sample desorption.

[0005] Therefore, striking a balance between sensitivity and resolution has always been a pressing issue in MSI analysis. [Summary of the Invention]

[0006] This application provides a brain tissue imaging method, apparatus, computer equipment, and storage medium, which can improve the spatial resolution of mass spectrometry images of brain tissue slices.

[0007] The first aspect of this application provides an imaging method for brain tissue, comprising:

[0008] Determine the positional parameters of the fine probe corresponding to the target brain tissue slice;

[0009] The target spray solvent is controlled to bombard the surface of the target brain tissue slice at a predetermined flow rate within the fine probe, so that the target brain tissue slice is desorbed and ionized;

[0010] The fine probe is controlled to scan within the target brain tissue slice after desorption and ionization at a preset step size according to the position parameters, so as to obtain the raw mass spectrometry data corresponding to the target brain tissue slice.

[0011] The mass spectrometry image corresponding to the target brain tissue slice is determined based on the raw mass spectrometry data corresponding to the target brain tissue slice.

[0012] A second aspect of this application provides an imaging device for brain tissue, comprising:

[0013] The determining unit is used to determine the positional parameters of the fine probe corresponding to the target brain tissue slice;

[0014] A control unit is used to control the target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate within the fine probe, so that the target brain tissue slice is desorbed and ionized;

[0015] The scanning unit is used to control the fine probe to scan within the target brain tissue slice after desorption and ionization at a preset step size according to the position parameters, so as to obtain the raw mass spectrometry data corresponding to the target brain tissue slice.

[0016] An imaging unit is used to determine the mass spectrometry image corresponding to the target brain tissue slice based on the raw mass spectrometry data corresponding to the target brain tissue slice.

[0017] In one possible design, the imaging unit is specifically used for:

[0018] The raw mass spectrometry data corresponding to the target brain tissue slices are converted to the target format data.

[0019] The target format data is reconstructed to generate a mass spectrometry image corresponding to the target brain tissue slice.

[0020] In one possible design, the imaging unit is further used for:

[0021] The background of the mass spectrometry image corresponding to the target brain tissue slice is removed to obtain the target mass spectrometry image;

[0022] Ion information is extracted from a preset region in the target mass spectrometry image.

[0023] In one possible design, the imaging unit is further used for:

[0024] The ion information within the preset region is matched with a preset metabolic database to identify the metabolite information corresponding to the target brain tissue slice.

[0025] In one possible design, the positional parameters include the distance between the fine probe and the target brain tissue slice, the tilt angle of the fine probe relative to the target brain tissue slice, and the distance by which the capillaries within the fine probe extend beyond the fine probe. The target spray solvent is ACN / H2O, the ratio of ACN / H2O is 9:1, and the preset flow rate is 5 μL / min.

[0026] A third aspect of this application provides a data processing apparatus, which includes at least one connected processor, memory, and transceiver, wherein the memory is used to store program code, and the processor is used to call the program code in the memory to execute the steps of the brain tissue imaging method described in the first aspect.

[0027] A fourth aspect of this application provides a computer storage medium including instructions that, when executed on a computer, cause the computer to perform the steps of the brain tissue imaging method described in any of the preceding aspects.

[0028] Compared with related technologies, in the embodiments provided in this application, the brain tissue imaging device optimizes the probe position parameters, spray solvent composition and spray solvent flow rate when performing mass spectrometry imaging on brain tissue slices, and effectively improves the spatial resolution by reducing the moving step size, thereby improving the spatial resolution of the mass spectrometry image of the brain tissue slices. [Attached Image Description]

[0029] Figure 1 This is a schematic diagram of the system architecture of the brain tissue imaging system provided in the embodiments of this application;

[0030] Figure 2 This is a schematic diagram of the structure of the fine probe provided in the embodiments of this application;

[0031] Figure 3 A schematic diagram illustrating high-resolution AFADESI-MSI condition optimization provided for embodiments of this application;

[0032] Figure 4 This is a schematic diagram of the MSI image corresponding to the target brain tissue slice provided in the embodiments of this application;

[0033] Figure 5A schematic diagram illustrating the sensitivity and coverage of HR-MSI provided for embodiments of this application;

[0034] Figure 6 A schematic diagram illustrating the specificity, dynamic detection range, and stability of HR-MSI provided in the embodiments of this application;

[0035] Figure 7 This is a virtual structural diagram of the brain tissue imaging device provided in the embodiments of this application;

[0036] Figure 8 This is a schematic diagram of the hardware structure of the server provided in an embodiment of this application.

Detailed Implementation Methods

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0038] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in this invention is merely a logical division; in practical applications, other division methods may be used. For example, multiple modules may be combined or integrated into another system, or some feature vectors may be ignored or not executed. Additionally, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interface, and the indirect couplings or communication connections between modules may be electrical or other similar forms, none of which are limited in this invention. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present invention.

[0039] The following describes the imaging method of brain tissue from the perspective of the imaging device for brain tissue. The imaging device for brain tissue can be a server or a service unit within a server, and there is no specific limitation.

[0040] Please refer to the following: Figure 1 , Figure 1 A flowchart illustrating the brain tissue imaging method provided in this application embodiment includes:

[0041] 101. Determine the position parameters of the fine probe corresponding to the target brain tissue slice.

[0042] In this embodiment, the brain tissue imaging device can determine the positional parameters of the fine probe corresponding to the target brain tissue. These positional parameters include the distance between the fine probe and the target brain tissue slice, the tilt angle of the fine probe relative to the target brain tissue slice, and the distance by which the capillary within the fine probe extends beyond the fine probe. Mass spectrometry images of the target brain tissue slice are obtained using an airflow-assisted desorption electrospray ionization (AFADESI) platform and a Q-Exactive mass spectrometer. The fine probe incorporates a TaperTip capillary with an inner diameter of 20 μm, which, compared to existing 100 μm probes, results in a smaller spray point, making it suitable for imaging samples with fine regions. Furthermore, to achieve optimal imaging results, the positional parameters of the fine probe for high-resolution imaging were optimized. Clear and stable imaging results can be obtained when the distance between the fine probe and the target brain tissue slice is 4 mm, the tilt angle of the fine probe relative to the target brain tissue is 60°, and the distance by which the capillary within the fine probe extends beyond the fine probe is 7 mm. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of the structure of the fine probe provided in the embodiment of this application, wherein the capillary 202 disposed inside the fine probe extends beyond the fine probe 201 by a distance of 7 mm.

[0043] It should be noted that, for ease of explanation, the following examples will use brain tissue slices corresponding to the mouse cerebellum as the target brain tissue slices. Before performing mass spectrometry imaging on the brain tissue, it is necessary to prepare the brain tissue slices, which will be explained in detail below:

[0044] After washing with 4°C physiological saline, the cerebellum was dissected and stored at -80°C for later use. For sectioning, the cerebellum tissue was removed from the -80°C cryogenic freezer, thawed at -20°C for 2 hours, and then sectioned to the appropriate thickness (i.e., target brain tissue sections, with 15μm for mass spectrometry imaging and 12μm for Nissl staining) using a CM1860 cryostat. These sections were then mounted on glass slides. The prepared target brain tissue sections were then stored at -80°C. After determining the corresponding mass spectrometry image of the target brain tissue sections, each section was dried in a -20°C vacuum desiccator for 1 hour, followed by drying at room temperature for 1 hour, and then subjected to AFADESI-MSI analysis.

[0045] The instruments used to prepare brain tissue sections and for subsequent analysis are as follows:

[0046] The system includes a two-dimensional ACQUITY UPLC I-Class liquid chromatograph equipped with a Waters ACQUITY UPLC HSS T3 column (2.1×100mm, 1.8μm); an AFADESI mass spectrometry imaging platform equipped with a MassImger Pro advanced mass spectrometry imaging system workstation; a Q-Extractive mass spectrometer equipped with an Xcalibur 3.0 data processing system; a self-developed aerodynamically assisted desorption / electrospray ionization device; a CM1860 cryostat; and a PC-3 vacuum desiccator.

[0047] 102. Control the target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate within the fine probe, so that the target brain tissue slice is desorbed and ionized.

[0048] In this embodiment, the brain tissue imaging device can control the target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate within a locked fine probe, so as to desorb and ionize the target brain tissue slice. The target spray solvent is ACN / H2O. The following is in conjunction with... Figure 3 A comparative analysis of the mass spectrometry images generated at different predetermined flow rates and the composition of the target spray solvent is presented:

[0049] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating the high-resolution AFADESI-MSI condition optimization provided in an embodiment of this application. Figure 3 A shows cerebellar MSI images using different ratios of ACN / H2O as the spray solvent. Figure 3 B is a schematic diagram showing the ionic strength of representative metabolites under different spray solvents. Figure 3 C is a schematic diagram of the ionic strength at m / z 104.1075 and m / z 303.2324 for different spray solvent flow rates. Figure 3 D is an optical schematic diagram of the p-brain scan width under different spray solvent flow rates. Figure 3 E is a schematic diagram of the width of white matter details in cerebellar MSI.

[0050] Experiments have shown that when the target spray solvent is ACN / H2O with a ratio of ACN to H2O of 9:1, the obtained MSI images are clear, have high contrast, and the cerebellar microregions are clearly visible. Furthermore, due to the increased proportion of the organic phase, the detection intensity of polar small molecule metabolites slightly decreases, while the detection sensitivity for fatty acids and lipid macromolecules improves (e.g., ...). Figure 3 (as shown in B).

[0051] In addition, brain tissue sections were scanned line by line using a liquid-phase stable delivery method with target spray solvent at flow rates ranging from 1 to 7 μL / min. The results showed that in the low flow rate range, the signal intensities of choline (m / z 104.1075) and arachidonic acid (m / z 303.2324) gradually increased with increasing flow rate in both positive and negative detection modes, reaching their maximum at 5 μL / min (e.g., ...). Figure 3 (As shown in C). The width of the etched region after a single-line scan was used to characterize the spray point size, and a positive correlation was found between solvent flow rate and spray point size (e.g., ...). Figure 3 As shown in D). At a spray solvent flow rate of 5 μL / min, the peel width exceeded 1000 μm, but a resolution of approximately 80 μm could still be obtained within a 100 μm step size in the imaging map. Figure 3 E). Therefore, when performing high-resolution imaging, a larger flow rate can be selected to obtain a reasonable resolution while improving detection sensitivity.

[0052] It should be noted that step 101 can determine the position parameters of the fine probe corresponding to the target brain tissue, and step 102 can control the target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate in the fine probe so that the target brain tissue slice is desorbed and ionized. However, there is no restriction on the order of these two steps. Step 101 can be performed first, or step 102 can be performed first, or they can be performed simultaneously. There is no specific limitation.

[0053] 103. Based on the position parameters, control the fine probe to scan within the desorbed and ionized target brain tissue slice at a preset step size to obtain the raw mass spectrometry data corresponding to the target brain tissue slice.

[0054] In this embodiment, the brain tissue imaging device can control a fine probe to scan within the target brain tissue slice after desorption and ionization at a preset step size according to position parameters, so as to obtain the raw mass spectrometry data corresponding to the target brain tissue slice.

[0055] 104. Determine the mass spectrometry image corresponding to the target brain tissue slice based on the raw mass spectrometry data corresponding to the target brain tissue slice.

[0056] In this embodiment, the brain tissue imaging device can first convert the raw mass spectrometry data corresponding to the target brain tissue slice into a target format data, and then reconstruct the target format data to generate a mass spectrometry image corresponding to the target brain tissue slice. That is, the brain tissue imaging device converts the collected .raw format raw mass spectrometry data into .cdf format data that can be recognized by the imaging software (this imaging software can be custom-developed imaging software, such as MassImager, a dedicated imaging software based on the C++ programming language; of course, it can also be other imaging software, as long as the raw mass spectrometry data is converted into a format that the imaging software can recognize; no specific limitation is made). Then, it performs image reconstruction to obtain a mass spectrometry image.

[0057] It should be noted that after obtaining the mass spectrometry image, the brain tissue imaging device can remove the background of the mass spectrometry image corresponding to the target brain tissue, thereby obtaining the target mass spectrometry image. Ion information from a preset region within the target mass spectrometry image can then be matched with a preset metabolic library to identify the metabolites corresponding to the target brain tissue slice. In other words, the brain tissue imaging device can remove the background and accurately extract the MS contour of a specific region by matching it with a high spatial resolution Nissl image. Simultaneously, it extracts the ion information of the MS contour and matches this ion information with a preset metabolic library to identify metabolites. This preset metabolic library includes, but is not limited to, metabolic libraries established using LC / GC-MS, human metabolomics databases, and metabolic libraries such as Metlin and LIPID MAPS. Using MATLAB code, the ion information extracted from the MSI is matched and filtered with the aforementioned metabolic libraries (m / z 70-150 error range <0.001 Da; m / z 150-1,000 error range <5 ppm).

[0058] In this embodiment, the brain tissue imaging device can set the inner capillary of the fine probe to face the ion source to obtain higher detection intensity, and solve the problems of MSI sensitivity and resolution through a point-by-point ablation scanning strategy. During the scanning process of the target brain tissue slice after desorption and ionization, controlled by position parameters, the target brain tissue slice is only resolved by the lower right edge of the spray point corresponding to the target spray solvent within the fine probe. The spatial resolution is mainly affected by the movement step size; reducing the movement step size can effectively improve the spatial resolution. Furthermore, when the spray point passes through the target brain tissue slice, resolution is complete, and there is virtually no sample residue on the slide after scanning. Therefore, increasing the sample resolution can improve the imaging sensitivity to a certain extent.

[0059] The following is a reference Figure 2 , Figures 4 to 6The imaging methods for brain tissue are explained using preset step sizes of 100μm and 40μm as examples.

[0060] Please see Figure 4 , Figure 4 This is a schematic diagram of the MSI image corresponding to the target brain tissue slice provided in the embodiments of this application. Figure 4 A is a schematic diagram showing the distribution of different ions at the 40μm and 100μm steps in the target brain tissue slice. Figure 4 B is a schematic diagram of the size of a single pixel in an image with a step size of 40μm. Figure 4 C is a schematic diagram of the Nissler staining corresponding to the target brain tissue slice:

[0061] Figure 4 In this study, MSI analysis was performed on target brain tissue slices with step sizes of 100 μm and 40 μm, respectively. Reducing the step size can effectively improve the spatial resolution of imaging. In the high-quality images obtained at a step size of 40 μm, the pixels are smaller and the tissue edges are clear. Different metabolite ions can be observed to be distributed in the gray matter, white matter and granular layer of the cerebellum. However, these features are masked at a step size of 100 μm. Among them, small molecule metabolites are more evenly distributed in various regions of the brain. Compared with lipids, the image quality is significantly improved after reducing the step size.

[0062] The resolution of MSI was calculated using different methods. The size of a single pixel in high-resolution imaging is 20μm × 40μm (e.g., Figure 4 As shown in B), the theoretical lateral resolution is 20 μm; compared with the Nissl staining results of adjacent slices, the width of the cerebellar white matter in fine areas is approximately 30 μm (as shown in B). Figure 4 As shown in C), the size of the image is consistent with it, indicating that the actual resolution can reach about 30 μm, which significantly improves the resolution of the mass spectrometry image.

[0063] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating the sensitivity and coverage of HR-MSI provided in an embodiment of this application, wherein... Figure 5 A is a schematic diagram showing the ionic strength of the metabolites in the two steps. Figure 5 B is a schematic diagram showing the number of ions detected and identified at step sizes of 40 μm and 100 μm. Figure 5 C is a schematic diagram showing the number of ions detected and identified in different m / z ranges with step sizes of 40 μm and 100 μm. The details are explained below:

[0064] Within the area traversed by the spray point corresponding to the target spray solvent in the fine probe, the tissue is essentially completely resolved. Increasing the amount of resolved sample per unit area can improve detection sensitivity. Intensity studies of several different types of metabolite ions under positive and negative ion modes revealed that the ionic intensity of small molecule metabolites such as taurine, carnitine, and spermine decreased less, while the detection sensitivity of fatty acids and lipid macromolecules decreased significantly, approximately three times (e.g., ...). Figure 5 (As shown in A).

[0065] By comparing the number of detectable metabolites (Intensity>1000) and the number of metabolites identified by matching with the metabolic library at two step sizes, statistical analysis showed that at step sizes of 100 μm and 40 μm, MSI detected 1547 and 1099 metabolite ions in positive and negative ion modes, respectively, and identified 535 and 406 metabolite ions (e.g., ...). Figure 5 (As shown in B), these metabolites include choline, polyamines, carnitine, amino acids, nucleosides, nucleotides, bases, organic acids, carbohydrates, cholesterol, bile acids, and lipids. These metabolites are widely distributed in different pathways of these metabolic networks. Furthermore, segmenting metabolites into different mass ranges revealed that as resolution increased, the coverage of small molecule metabolites was less affected, while the coverage of large molecule lipids decreased significantly (e.g., ...). Figure 5 (As shown in C). All the above results indicate that the developed high spatial resolution metabolomics method has good coverage and is especially suitable for the analysis of small molecule metabolites.

[0066] Please see Figure 6 , Figure 6 This is a schematic diagram illustrating the specificity, dynamic detection range, and stability of HR-MSI provided in an embodiment of this application. Figure 6 A is a superimposed image of the mass spectra at m / z 146.1174 and m / z 146.1654 for a single pixel. Figure 6 B is a schematic diagram of the MSI images at m / z 104.1072 and m / z 846.5370. Figure 6 C is a schematic diagram of the stability of the mass axis. Figure 6 D is a schematic diagram of the repeatability of the measurement.

[0067] like Figure 6 As shown in Figure B, the dynamic detection range of the established high-resolution imaging method was investigated. The peak signal intensity of the ion with a high concentration in the brain at m / z 104.1072 was 1.0E. 7 The peak signal intensity of the ion with lower content at m / z 846.5370 was 2.5E. 3The specific distributions of both are clearly visible in the imaging images. Further comparison of the highest ion peak intensities of different compounds revealed that the signal intensity difference between different compounds can reach 3-4 orders of magnitude, indicating that this method has a wide dynamic detection range, capable of detecting metabolite ions from trace amounts to extremely high concentrations, and can display the distribution differences of compounds in different micro-regions in the imaging images.

[0068] Based on the high mass resolution performance of the orbital trap mass spectrometer and the high resolution characteristics of the MassImager software, a mass spectrometry imaging analysis method was established to separate compounds and isotopes with similar masses. Figure 6 A shows the mass spectrum of a single pixel, containing rich ion information. The mass difference between ions m / z 146.1174 and m / z 146.1654 is only 0.01 Da, but they clearly show as two adjacent ion peaks in the mass spectrum, indicating a significant difference in the content of these two ions in the micro-region of the image.

[0069] Because high-resolution imaging requires long-term continuous scanning, the stability of the mass axis was investigated in the experiment. Ion data were extracted from different regions of the same slice, and the actual values ​​of metabolite ions were compared with the theoretical values. The results showed that the m / z fluctuation range was within ±0.0005 Da, indicating that the mass axis was relatively stable (e.g., Figure 6 (As shown in C). Furthermore, for method reproducibility, five adjacent slices were imaged, and the ROI was selected for statistical analysis of ion intensities. The results showed that the RSD of the ion peak intensities of different representative endogenous metabolites in adjacent slices was less than 15% (e.g., ...). Figure 6 As shown in D), this demonstrates that our established high-resolution imaging method is stable and reliable, and can meet the requirements of metabolomics analysis.

[0070] In summary, it can be seen that in the embodiments provided in this application, the brain tissue imaging device optimizes parameters including probe position, spray solvent composition and spray solvent flow rate when performing mass spectrometry imaging on brain tissue slices, and effectively improves spatial resolution by reducing the moving step size, thereby improving the spatial resolution of the mass spectrometry image of brain tissue slices.

[0071] The above describes the imaging methods for brain tissue; the following describes the imaging apparatus for brain tissue.

[0072] Please see Figure 7 , Figure 7 This is a virtual structural diagram of a brain tissue imaging device provided in an embodiment of this application. The brain tissue imaging device 700 includes:

[0073] The determining unit is used to determine the positional parameters of the fine probe corresponding to the target brain tissue slice;

[0074] A control unit is used to control the target spray solvent to bombard the surface of the target brain tissue slice at a predetermined flow rate within the fine probe, so that the target brain tissue slice is desorbed and ionized;

[0075] The scanning unit is used to control the fine probe to scan within the target brain tissue slice after desorption and ionization at a preset step size according to the position parameters, so as to obtain the raw mass spectrometry data corresponding to the target brain tissue slice.

[0076] An imaging unit is used to determine the mass spectrometry image corresponding to the target brain tissue slice based on the raw mass spectrometry data corresponding to the target brain tissue slice.

[0077] In one possible design, the imaging unit is specifically used for:

[0078] The raw mass spectrometry data corresponding to the target brain tissue slices are converted to the target format data.

[0079] The target format data is reconstructed to generate a mass spectrometry image corresponding to the target brain tissue slice.

[0080] In one possible design, the imaging unit is further used for:

[0081] The background of the mass spectrometry image corresponding to the target brain tissue slice is removed to obtain the target mass spectrometry image;

[0082] Ion information is extracted from a preset region in the target mass spectrometry image.

[0083] In one possible design, the imaging unit is further used for:

[0084] The ion information within the preset region is matched with a preset metabolic database to identify the metabolite information corresponding to the target brain tissue slice.

[0085] In one possible design, the positional parameters include the distance between the fine probe and the target brain tissue slice, the tilt angle of the fine probe relative to the target brain tissue slice, and the distance by which the capillaries within the fine probe extend beyond the fine probe. The target spray solvent is ACN / H2O, the ratio of ACN / H2O is 9:1, and the preset flow rate is 5 μL / min.

[0086] Figure 8 This is a schematic diagram of the server structure in this application. The server can be integrated within a brain tissue imaging device, such as... Figure 8As shown, the server 800 in this embodiment includes at least one processor 801, at least one network interface 804 or other user interface 803, a memory 805, and at least one communication bus 802. The server 800 may optionally include the user interface 803, including a display, keyboard, or clicking device. The memory 805 may include high-speed RAM, or it may also include non-volatile memory, such as at least one disk storage device. The memory 805 stores execution instructions. When the server 800 is running, the processor 801 communicates with the memory 805, and the processor 801 calls the instructions stored in the memory 805 to execute the aforementioned brain tissue imaging method. The operating system 806 includes various programs for implementing various basic services and processing tasks according to the hardware.

[0087] The server provided in this application embodiment can execute the technical solutions of the above-described brain tissue imaging method embodiments. Its implementation principle and technical effects are similar, and will not be repeated here.

[0088] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a computer, implements the method flow related to the brain tissue imaging device in any of the above method embodiments. Correspondingly, the computer can be the aforementioned brain tissue imaging device.

[0089] This invention also provides a computer program or a computer program product including a computer program, which, when executed on a computer, causes the computer to implement the method flow related to the brain tissue imaging device in any of the above method embodiments. Correspondingly, the computer can be the aforementioned brain tissue imaging device.

[0090] In the above Figure 1 In the illustrated embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0091] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0092] It should be understood that the processor mentioned in this invention can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0093] It should also be understood that the number of processors in this invention can be one or more, and can be adjusted according to the actual application scenario. This is merely an illustrative example and is not intended to limit the scope. Similarly, the number of memories in this embodiment of the invention can be one or more, and can be adjusted according to the actual application scenario. This is merely an illustrative example and is not intended to limit the scope.

[0094] It should also be noted that when the brain tissue imaging device includes a processor (or processing unit) and a memory, the processor in this invention can be integrated with the memory, or the processor and the memory can be connected through an interface. The specific configuration can be adjusted according to the actual application scenario and is not limited.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or other device, etc.) to execute the present invention. Figure 1 All or part of the steps of the brain tissue imaging method described above.

[0100] It should be understood that the storage medium or memory mentioned in this invention may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0101] It should be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0102] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An imaging method for brain tissue, characterized in that, include: Determine the positional parameters of the fine probe corresponding to the target brain tissue slice; The target spray solvent is controlled to bombard the surface of the target brain tissue slice within the fine probe at a predetermined flow rate, so that the target brain tissue slice is desorbed and ionized. The target spray solvent is ACN / H2O, the ratio of ACN / H2O is 9:1, and the predetermined flow rate is 5 μL / min. According to the position parameters, the fine probe is controlled to scan within the target brain tissue slice after desorption and ionization at a preset step size to obtain the raw mass spectrometry data corresponding to the target brain tissue slice. The preset step size is 40 μm, which ensures complete resolution when the spray point passes through the target brain tissue slice, and leaves no sample residue on the slide after scanning. The mass spectrometry image corresponding to the target brain tissue slice is determined based on the raw mass spectrometry data corresponding to the target brain tissue slice.

2. The method according to claim 1, characterized in that, The step of determining the mass spectrometry image corresponding to the target brain tissue slice based on the raw mass spectrometry data corresponding to the target brain tissue slice includes: The raw mass spectrometry data corresponding to the target brain tissue slices are converted to the target format data. The target format data is reconstructed to generate a mass spectrometry image corresponding to the target brain tissue slice.

3. The method according to claim 1, characterized in that, The method further includes: The background of the mass spectrometry image corresponding to the target brain tissue slice is removed to obtain the target mass spectrometry image; Ion information is extracted from a preset region in the target mass spectrometry image.

4. The method according to claim 3, characterized in that, The method further includes: The ion information within the preset region is matched with a preset metabolic database to identify the metabolite information corresponding to the target brain tissue slice.

5. The method according to any one of claims 1 to 4, characterized in that, The positional parameters include the distance between the fine probe and the target brain tissue slice, the tilt angle of the fine probe relative to the target brain tissue slice, and the distance by which the capillaries within the fine probe extend beyond the fine probe.

6. An imaging device for brain tissue, characterized in that, include: The determining unit is used to determine the positional parameters of the fine probe corresponding to the target brain tissue slice; A control unit is used to control the target spray solvent to bombard the surface of the target brain tissue slice within the fine probe at a predetermined flow rate, so that the target brain tissue slice is desorbed and ionized, wherein the target spray solvent is ACN / H2O, the ratio of ACN / H2O is 9:1, and the predetermined flow rate is 5 μL / min; The scanning unit is used to control the fine probe to scan within the target brain tissue slice after desorption and ionization at a preset step size according to the position parameters, so as to obtain the raw mass spectrometry data corresponding to the target brain tissue slice. The preset step size is 40 μm, which allows for complete resolution when the spray point passes through the target brain tissue slice, and leaves no sample residue on the slide after scanning. An imaging unit is used to determine the mass spectrometry image corresponding to the target brain tissue slice based on the raw mass spectrometry data corresponding to the target brain tissue slice.

7. The apparatus according to claim 6, characterized in that, The imaging unit is specifically used for: The raw mass spectrometry data corresponding to the target brain tissue slices are converted to the target format data. The target format data is reconstructed to generate a mass spectrometry image corresponding to the target brain tissue slice.

8. The apparatus according to claim 6, characterized in that, The imaging unit is also used for: The background of the mass spectrometry image corresponding to the target brain tissue slice is removed to obtain the target mass spectrometry image; Ion information is extracted from a preset region in the target mass spectrometry image.

9. A data processing device, characterized in that, include: At least one connected processor, memory, and transceiver, wherein the memory is used to store program code, and the processor is used to invoke the program code in the memory to perform the steps of the brain tissue imaging method according to any one of claims 1 to 5.

10. A computer storage medium, characterized in that, include: Instructions, when executed on a computer, cause the computer to perform the steps of the brain tissue imaging method according to any one of claims 1 to 5.

Images