Multi-target molecule mass spectrum imaging and in-situ identification method and device thereof

Abstract

The invention relates to the technical field of biochemical analysis, in particular to a multi-target molecule mass spectrum imaging and in-situ identification method and device. According to the specific technical scheme, the method comprises the following steps: (1) collecting primary mass spectrum data of a slice sample; (2) carrying out continuous clustering analysis on the primary mass spectrum data to obtain a molecular characteristic sub-region distribution diagram with different components; (3) collecting cascade mass spectrometry data of the sample sampling point extract according to the molecular characteristic sub-region distribution diagram information; and (4) realizing accurate identification of an imaging result according to accurate mass number information and tandem mass spectrum data in the primary mass spectrum data. According to the method, the imaging data can be synchronously acquired and processed through a data processing and multi-clustering method, so that the spatial clustering information of the chemical components is efficiently and quickly obtained, the chemical structure sub-region of the characteristic is found, the cascade mass spectrum data acquisition is guided, and the in-situ molecular imaging and identification are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of biochemical analysis, and in particular to a method and device for multi-target molecular mass spectrometry imaging and in-situ identification. Background Art

[0002] Ionization technology is driving the development of mass spectrometry imaging technology. Different ionization technologies, combined with different imaging scanning techniques, have formed a variety of mass spectrometry imaging technologies. In principle, desorption electrospray ionization uses charged microdroplets generated by electrospray ionization to act on the sample surface, desorbing and ionizing analyte molecules; secondary ion ionization uses primary ions to bombard the sample surface, sputtering analyte molecules; matrix-assisted laser desorption ionization uses focused laser irradiation on the "matrix-sample" interface to excite analyte molecules; dielectric barrier discharge ionization applies plasma to the sample surface, sweeping and ionizing analyte molecules; and probe electrospray ionization uses a solid probe to contact the sample surface for sampling and then ionizes the analyte molecules.

[0003] The ionization probe of the mass spectrometry imaging platform acts sequentially on the sampling points on the sample slice to generate charged ions that can be detected by mass spectrometry. However, due to the small size of a single sampling point and the fact that the current ionization probe can only excite instantaneous charged ions from a single sample slice sampling point, the mass spectrometer can only collect a small amount of primary mass spectrometry data. Therefore, the current mass spectrometry imaging technology can usually only collect a few frames of primary full scan mass-to-charge ratio data (MS 1 ).

[0004] High-resolution mass spectrometry imaging technology uses precise mass-to-charge ratio information to compare with molecules in the database to directly identify molecules. It is suitable for imaging analysis of some known exogenous drugs (the sample slice does not contain interference from substances with the same mass number). However, for imaging analysis to identify unknown molecules, it is difficult to rely solely on MS due to the existence of isomers. 1 There is great uncertainty in molecular identification. Tandem mass spectrometry data contains fragment ions that contain molecular structure information, and the use of tandem mass spectrometry data can significantly improve the accuracy and reliability of molecular identification. Therefore, targeted mass spectrometry imaging technology uses modes such as multiple reaction monitoring (MRM) and selected reaction monitoring (SRM) to accurately image and analyze specific molecules. In addition, for non-targeted analysis tasks, when the sample volume is large, tandem mass spectrometry data can be collected from the slices instead of being analyzed by LC-MS / MS on the tissue homogenate. This method requires additional workflow and analysis time, and may also cause the signals of some low-abundance analytes in the tissue homogenate to be masked by high-abundance analytes, resulting in the loss of tandem mass spectrometry data for low-abundance analytes.

[0005] For the analysis of unknown chemical components, in situ collection of tandem mass spectrometry data from tissue sections can directly analyze local characteristic substances in the sections, avoiding the interference of global high-abundance substances, simplifying the workflow and shortening the analysis time. In the mass spectrometry imaging process, the data dependent acquisition (DDA) mode of the mass spectrometer can be used instead of the full scan mode to collect MS in situ from the sections. 1 and MS 2 Data. Saleh et al. (Khalil, SM; Sprenger, RR; Hermansson, M.; Ejsing, CSDDA-Imaging with Structural Identification of Lipid Molecules on an Orbitrap Velos pro Mass Spectrometer. J. Mass Spectrom. 2022, 57(9), e4882.) used MALDI sequential sampling and DDA to obtain the distribution information of 73 lipid chemicals from mouse brain. Lee et al. (Perdian, DC; Lee, YJ Imaging MS Methodology for More Chemical Information in Less Data Acquisition Time Utilizing a Hybrid Linear Ion Trap-orbitrap Mass Spectrometer. Anal. Chem. 2010, 82(22), 9393-9400.) used MALDI multiple spiral sampling and DDA to accurately analyze the spatial distribution of flavonoids in Arabidopsis petals. Generally speaking, a sampling point can only collect a small number of spectra during mass spectrometry imaging. Therefore, the imaging results in this mode either sacrifice the imaging resolution (reducing the original MS 1 The sampling point was changed to MS 2 ), or the acquisition time of mass spectrometry imaging needs to be doubled (in the original MS 1 New MS around the sampling point 2 sampling point).

[0006] Various types of mass spectrometry imaging techniques have certain complementarities due to their different ionization characteristics. Liquid extraction surface analysis (LESA) can extract sample molecules from the surface of a slice, ionize them, and collect tandem mass spectrometry data. However, the LESA extraction spot size is large, and the resolution is low when used alone for imaging. Therefore, combining LESA with other mass spectrometry imaging methods can obtain higher-resolution imaging images while also collecting tandem mass spectrometry data of the analyte. For example, Anderton et al. ( D.; Chu, RK; Carrell, AA; Thomas, M.; L.;Weston,DJ;Anderton,CRMultimodal MSI inConjunction with Broad Coverage Spatially Resolved MS2 Increases Confidencein Both Molecular Identification and Localization.Anal.Chem.2018,90(1),702-707.) Firstly, MALDI mass spectrometry imaging was used to analyze the triple symbiotic system of moss, cyanobacteria and fungi, and then LESA-MSI was used to collect MS in a grid sampling mode on the whole slice. 2Data, the combination of these two methods has reliably annotated and located dozens of metabolites in the symbiotic system. Goodwin et al. (Swales, JG; Tucker, JW; Spreadborough, MJ; Iverson, SL; Clench, MR; Webborn, PJH; Goodwin, RJA Mapping Drug Distribution in Brain Tissue Using Liquid Extraction Surface Analysis Mass Spectrometry Imaging. Anal. Chem. 2015, 87 (19), 10146-10152.) used MALDI-MSI and LESA-MSI to study the distribution of "permeable" and "non-permeable" drugs in the mouse brain. When LESA uses a grid sampling method to perform indiscriminate sampling and analysis of all pixels in the entire slice, a long mass spectrometry acquisition time is required. By using the optical information characteristics of the slice, tandem mass spectrometry analysis can be performed only on specific structural areas, which can significantly shorten the analysis time. Sweedler et al. (Comi, TJ; Makurath, MA; Philip, MC; Rubakhin, SS; Sweedler, JVM) used MALDI MS Guided Liquid Microjunction Extraction for Capillary Electrophoresis–Electrospray Ionization MS Analysis of Single Pancreatic Islet Cells. Anal. Chem. 2017, 89(14), 7765-7772.) used MALDI MSI to analyze the distribution of metabolites in rat pancreatic islet cells on a glass slide and then used LESA combined with capillary electrophoresis mass spectrometry to collect MS from the located pancreatic islet cells. 2 Data. Muddiman et al. (Pace, CL; Simmons, J.; Kelly, RT; Muddiman, DC Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS / MS. J. Proteome Res. 2022, 21(3), 713-720.) used MALDI MSI to image and analyze rat brain slices, and then used NanoPOTS technology to perform liquid chromatography-mass spectrometry analysis on small areas of specific brain physiological substructures and collect MS 2However, such methods require the assistance of sophisticated instruments and the careful manipulation of professional experimenters, and it takes a very long time to complete the chromatographic mass spectrometry analysis of multiple sampling points.

[0007] Therefore, for molecular identification, either only structural data can be collected from a few molecules on the slice (MRM / SRM mode MSI); or tandem mass spectrometry data cannot be collected for low-abundance substances (MSI + tissue homogenate LC-MS / MS); or the imaging resolution is low (LESA MSI); or it is time-consuming and requires high professional skills (MSI + local extraction LC-MS / MS, MSI + LESA). Summary of the Invention

[0008] In view of the shortcomings of the existing technology, the present invention provides a method and device for multi-target molecular mass spectrometry imaging and in situ identification.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] The present invention discloses a method for multi-target molecular mass spectrometry imaging and in-situ identification, comprising the following steps:

[0011] (1) placing the slice sample on an imaging analysis platform, and collecting primary mass spectrometry data of the slice sample in full scan mode using a mass spectrometry imaging module;

[0012] (2) Performing continuous cluster analysis on the primary mass spectrometry data to obtain subregional distribution maps of molecular features with different composition;

[0013] (3) Based on the molecular feature sub-region distribution map information, the liquid extraction surface analysis module is used to perform surface extraction on each region, and ionization is performed at the ion inlet of the mass spectrometer to collect tandem mass spectrometry data of the extract at the sample sampling point;

[0014] (4) Accurate identification of imaging results is achieved based on the accurate mass information in the primary mass spectrometry data and the tandem mass spectrometry data.

[0015] Preferably, in step (1), the mass spectrometry imaging module uses a mass spectrometry imaging mode selected from the group consisting of Nano DESI, MALDIMSI, and SIMS MSI.

[0016] Preferably, in step (3), the electrospray ionization technology used in the liquid extraction surface analysis module is one of Chain ESI, Nano ESI and In ESI.

[0017] Preferably, in step (2), the cluster analysis includes a dimensionality reduction algorithm and a clustering algorithm.

[0018] Preferably, the dimensionality reduction algorithm is FA, and the clustering algorithm is Kmeans or GMM.

[0019] Accordingly, a device for multi-target molecular mass spectrometry imaging and in-situ identification includes a device switching platform, on which an imaging device and a liquid extraction surface analysis module are disposed, the imaging device including a mass spectrometry imaging module, the mass spectrometry imaging module including a Venturi electrospray device, the Venturi electrospray device including a solvent delivery pipeline, the solvent delivery pipeline being outer-circuited with a compressed gas pipeline, and an ionization voltage being applied to the solvent delivery pipeline; by introducing compressed gas, a negative pressure is formed at the front end of the nozzle of the solvent delivery pipeline, and a solution at an extraction point on the precision displacement platform is continuously extracted through the solvent delivery pipeline and ionized at the ion inlet of the mass spectrometer;

[0020] The liquid extraction surface analysis module includes a liquid extraction surface analysis device, which drives the device to switch the carrier, and places the spray capillary on the liquid extraction surface analysis device perpendicular to the precision displacement platform. Under capillary action, the extraction solvent is sucked into the spray capillary, and the spray capillary is ionized at the ion inlet of the mass spectrometer by adjusting the driving device to switch the carrier.

[0021] Preferably, a liquid supply capillary is provided on the precision displacement platform, and the solution delivery pipeline and the liquid supply capillary form a liquid bridge at the contact position.

[0022] Preferably, the liquid supply capillary is fixed on the No. 1 three-dimensional platform, the device switching carrier is fixed on the No. 2 three-dimensional platform, the precision displacement platform and the No. 1 three-dimensional platform are fixed on the No. 3 three-dimensional platform, and the No. 3 three-dimensional platform is connected to the No. 2 three-dimensional platform.

[0023] The present invention has the following beneficial effects:

[0024] Compared with the target area MS based on slice optical structure 2 Analysis, multiple clustering can find areas with different chemical compositions on the slices, thereby more intuitively improving MS 2 Data on MS 1 In addition, by developing supporting data processing and multi-clustering software, it is possible to simultaneously collect and process imaging data, thereby efficiently and quickly obtaining spatial clustering information of chemical components, and then discovering characteristic chemical structure sub-regions to guide Chain-ESILESA analysis. Using this method, a good imaging analysis of rat brain tissue was performed, and the MS images generated from the same sample slice were 1 and MS 2 140 endogenous metabolites including amino acids and lipids were identified in the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a brief structural diagram of the device of the present invention;

[0026] Figure 2 This is the 3D atlas of the mouse brain in Example 1;

[0027] Figure 3 Clustering effects under different working modes; (a) absolute intensity before background removal, (b) absolute intensity after background removal, (c) relative intensity after background removal (relative to the intensity of the base peak), (d) internal standard correction intensity after background removal;

[0028] Figure 4 This is a comparison of different dimensionality reduction algorithms (all clustering algorithms use GMM);

[0029] Figure 5 This is a comparison of different clustering algorithms (all dimensionality reduction algorithms use FA);

[0030] Figure 6 Finding chemical signature subregions for sequential cluster analysis;

[0031] Figure 7 The imaging result diagram corresponding to Table 1;

[0032] Figure 8 A brief view of the positional relationship and connection relationship among the No. 1 3D platform, the No. 2 3D platform, and the No. 3 3D platform;

[0033] In the figure: precision displacement platform 1, liquid supply capillary 2, Venturi electrospray device 3, liquid extraction surface analysis device 4, device switching carrier 5, mass spectrometer 6, solvent delivery pipeline 7, spray capillary 8, compressed gas pipeline 9, air inlet channel 10. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0035] Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0036] The present invention has developed a cluster resampling mass spectrometry imaging (CR MSI) technology based on Chain-ESI, which integrates Nano DESI and Chain-ESI LESA into one platform, ensuring the stability and consistency of analysis to the greatest extent. 1 The data were continuously clustered to find the unique chemical structure sub-regions on the sample slices. Then, sampling points of interest were selected from these chemical structure sub-regions. The Chain-ESI-based LESA technology (Chain-ESI LESA) was used to continuously ionize the trace extract of a single sampling point, thereby collecting a large number of MS 2 data.

[0037] The present invention discloses a device for multi-target molecular mass spectrometry imaging and in-situ identification, comprising a precision displacement platform 1 for carrying analytical samples, driven by a linear stepper motor to adjust the samples on the precision displacement platform 1 in the x and y directions. The device also comprises a device switching platform 5, on which an imaging device and a liquid extraction surface analysis module are provided. The imaging device comprises a mass spectrometry imaging module, which comprises a Venturi electrospray device 3. The Venturi electrospray device 3 comprises a solvent delivery pipe 7, which is provided with a compressed gas pipe 9 on the outer surface, and an ionization voltage is applied to the solvent delivery pipe 7. Compressed gas is introduced through an air inlet channel 10 in the middle of the Venturi electrospray device 3, forming a negative pressure at the front end of the nozzle of the solvent delivery pipe 7, continuously extracting the solution at the extraction point on the precision displacement platform 1 through the solvent delivery pipe 7 and ionizing it at the ion inlet of the mass spectrometer 6.

[0038] The liquid extraction surface analysis module includes a liquid extraction surface analysis device 4, which drives the device to switch the carrier 5, and places the spray capillary 8 on the liquid extraction surface analysis device 4 perpendicular to the precision displacement platform 1. Under capillary action, the extraction solvent is sucked into the spray capillary 8, and the device is driven to switch the carrier 5, so that the spray capillary 8 is ionized at the ion entrance of the mass spectrometer 6.

[0039] Specific: Reference Figure 1 ,Nano DESI( Figure 1a) A liquid supply capillary 2 (quartz) with an outer diameter of 186 μm and an inner diameter of 100 μm is used to supply the extraction solvent, and a Venturi electrospray device 3 is used to continuously extract the extraction solvent and spray ionize it. The Venturi electrospray device 3 adopts a double-layer sleeve mode, with the inner layer using a stainless steel capillary (outer diameter 250 μm, inner diameter 150 μm, length 100 mm) as the solution delivery pipeline 7, and the outer layer using a borosilicate glass capillary with an inner diameter of 0.9 mm as the compressed gas pipeline 9. The solution delivery pipeline 7 and the liquid supply capillary 2 form a liquid bridge at the contact position. The gas can be nitrogen. Under the action of compressed nitrogen, a negative pressure is formed at the front end of the nozzle of the Venturi electrospray device 3, continuously extracting and ionizing the solvent at the extraction point at the tail end. As one embodiment, the pressure of compressed nitrogen is 0.4 MPa; the extraction solvent is a 90% methanol aqueous solution containing 0.5% formic acid (50 ppb of clozapine is added as an internal standard); the supply rate of the extraction solvent is 30 μL / min (air pressure pump, the solution flow rate is about 30 μL / min at 0.1 MPa); the moving scanning speed of the precision displacement platform 1 is 10 mm / min; and the ionization voltage of 3 kV is loaded on the stainless steel capillary (i.e., the solution delivery pipeline 7).

[0040] The Chain-ESI LESA device is driven by a steering gear (a steering gear can be understood as a motor that accurately controls the rotation angle or position by receiving a control signal) to switch the stage 5 to achieve the sampling angle ( Figure 1 b) and ionization angle ( Figure 1 c) is switched, and a micro linear motor is used to control the distance between the tip of the spray capillary 8 and the sample surface, as well as the distance between the tip of the spray capillary 8 and the ion inlet of the mass spectrometer 6 during ionization. During sampling, the spray capillary 8 of the Chain-ESI LESA is perpendicular to the sample surface, and the solvent is supplied by the same liquid supply capillary 2 of the imaging device (such as Nano DESI) (Shimadzu high-pressure liquid phase pump, 1μL / min continuous supply for 1min). Under the capillary action, the extraction solvent is sucked into the spray capillary 8. After the solvent supply stops for 30s, the spray capillary 8 is lifted away from the sample surface and rotated to the ionization position. When the spray capillary 8 is ionized, it is about 1mm away from the ion inlet of the mass spectrometer 6. The primary ionization source is turned on to generate the charge that excites the secondary electrospray, so that the extraction solvent generates ultra-low flow rate electrospray ionization at the tip of the spray capillary 8 ( Figure 1 c) The ionization mode of LESA is Chain-ESI mode. Chain-ESI LESA ionizes at the ionization position and the mass spectrometer collects MS 2 Data, stop ionization after two minutes, disconnect the high voltage of the primary ionization source and rotate the spray capillary away from the ion inlet of the mass spectrometer. Under the action of nitrogen (0.4 MPa for 1 s), remove the remaining liquid in the spray capillary 8 to reduce the residual effect and avoid interference with the analysis of the next extraction point.

[0041] Furthermore, the switching between Chain-ESI LESA and Nano DESI is controlled by the device switching stage 5. Integrating the two analysis modes on the same analysis platform is the key to ensuring in-situ analysis of slices. Figure 8 As shown, the entire apparatus utilizes three manually adjustable 3D stages (e.g., a manual 3D displacement stage with a micrometer, such as the LTP40-LM) to achieve fine positional adjustments for Chain-LESA and Nano DESI. 3D stage 1 adjusts the spatial relationship of the liquid supply capillary 2 relative to the imaging scanning stage (i.e., precision displacement stage 1). The liquid supply capillary 2 is fixed to 3D stage 1. 3D stage 2 adjusts the spatial relationship of the Chain-ESI LESA and Nano DESI relative to the mass spectrometer's ion inlet. The device switching stage 5 is fixed to 3D stage 2. The precision displacement platform 1 and the 3D platform 1 are fixed to the 3D platform. Specifically, the upper end of the 3D platform 1 is connected to the lower end of the 3D platform 1. Simultaneously, the lower end of the 3D platform 1 is connected to the lower end of the 2D platform via a bracket. Here, the upper end refers to the spatially adjustable platform on the 3D platform, and the lower end refers to the base of the 3D platform. Similarly, brackets connect the 1D platform 2 to the liquid supply capillary 2, and the 2D platform 5 to the device switching carrier. The brackets' connection conditions and shapes are configured and connected according to actual needs. The 3D platform 3 allows for adjustment of the extraction liquid bridge to ensure its stability (both Nano DESI and Chain-ESI LESA require an extraction liquid bridge for sampling). During Nano DESI mass spectrometry imaging analysis, the nozzle of the Venturi electrospray device 3 is adjusted relative to the mass spectrometer's ion inlet using the second three-dimensional platform. The liquid supply capillary 2 is then adjusted to the glass slide surface on the imaging scanning platform (i.e., precision displacement platform 1) using the first three-dimensional platform. Finally, the precision displacement platform 1 is adjusted to align the liquid supply capillary 2 with the stainless steel capillary (solution delivery line 7) to form a stable extraction liquid bridge. After the imaging analysis is complete, the analysis platform is adjusted to Chain-ESI LESA mode using the servo. The ionization position is adjusted using the second three-dimensional platform, and the sampling position is aligned using the third three-dimensional platform. The first three-dimensional platform remains unchanged to ensure that Nano DESI and Chain-ESI LESA are positioned at the same sampling point, as indicated by the liquid supply capillary.

[0042] The complete CR MSI workflow is as follows:

[0043] (a) Place the slice sample on the imaging analysis platform and then use Figure 1 The Nano DESI device shown in the figure is used to collect first-stage full-scan mass spectrometry data.

[0044] (b) After completing the first-stage full-scan mass spectrometry data acquisition using Nano DESI, the data were subjected to continuous cluster analysis to obtain a sub-regional distribution map of molecular features with fine structure and distinct composition;

[0045] (c) Based on the molecular characteristic sub-region distribution map of the slice sample, Chain-ESI LESA was used to perform surface extraction on each block ( Figure 1 b) Then rotate to the ionization position for ionization, and collect tandem mass spectrometry data of a small amount of sample sampling point extract to improve the accuracy of subsequent metabolite identification. Because spatial chemical partitioning is divided according to chemical composition, each block has different chemical composition (compound type or content). Therefore, collecting tandem data for each block separately can theoretically collect more types of tandem mass spectrometry data, thereby improving the coverage of metabolite identification;

[0046] (d) Accurately identify the imaging results based on the accurate mass information in the primary mass spectrometry data and the tandem mass spectrometry data obtained by imaging scanning.

[0047] Furthermore, the Nano DESI used in the present invention can be replaced by other types of mass spectrometry imaging methods, such as MALDIMSI, SIMS MSI, etc., which can collect full-scan mass spectrometry data of slice samples and achieve the same effect.

[0048] The Chain ESI used in the present invention can also be replaced by other types of ionization methods, such as Nano ESI, InESI, etc., which can achieve the same effect.

[0049] The present invention employs dimensionality reduction and clustering algorithms to identify molecular feature subregions, thereby classifying the imaging results into distinct molecular feature subregions. Similar results can be achieved by using machine learning and artificial intelligence algorithms to classify and identify mass spectrometry imaging data to obtain multiple molecular feature subregions.

[0050] Furthermore, the present invention investigates various algorithms in dimensionality reduction and clustering algorithms. Different dimensionality reduction algorithms and data processing methods have an impact on the quality of clustering analysis results. The clustering effects under different working modes are compared. Figure 3As shown in a, {PCA:30, GMM:20} means that the PCA dimensionality reduction algorithm is used to reduce the dimension of the intensity matrix to 30, and then the GMM unsupervised clustering algorithm is used to divide each pixel into 20 categories. Other abbreviations are principal component analysis (PCA), uniform manifold approximation and projection (UMAP), factor analysis (FA) and Gaussian mixture model (GMM). For the sake of convenience, cluster analysis is used to refer to the calculation process of "dimensionality reduction and clustering". When background pixels are not excluded, all pixels will be clustered. However, the focus of the experiment is on the sample slice itself, and background interference will affect the clustering results. After removing the background, the quality of the cluster analysis is improved ( Figure 3 b). Furthermore, when using the relative intensity of mass spectra for cluster analysis, the factor analysis dimensionality reduction algorithm showed better partitioning results compared with other methods ( Figure 3 c). Figure 3 Figure d shows the cluster analysis results after using an internal standard (50 ppb clozapine solution) to correct for ion intensities. The quality of the spectra obtained by all three dimensionality reduction algorithms improved. Therefore, in subsequent studies, internal standards were used to correct ion intensities and remove background pixel interference from cluster analysis.

[0051] Figure 4 Nine dimensionality reduction algorithms were compared in the paper (each dimensionality reduction algorithm reduces the original data to 30 dimensions, uses GMM for clustering, and the number of clusters is 20, that is, {***:30, GMM:20}. Fast independent component analysis (FICA); T-distributed stochastic neighbor embedding (TSNE), whose dimensionality reduction number can only be 2; Non-Negative matrix factorization (NMF); Truncated singular value decomposition (TSVD); Kernel principal component analysis (KPCA); Sparse principal component analysis (SPCA)). The best clustering effect was achieved when the FA dimensionality reduction algorithm was used (and Figure 3 The conclusion is consistent).

[0052] Next, we further compared different clustering algorithms (K-means clustering (KMeans), density-based spatial clustering of applications with noise (DBSCAN), balanced iterative reducing and clustering using hierarchies (Brich), agglomerative custering (Agglo), mean shift (MeanShift), ordering points to identify the clustering structure (OPTICS), spectral biclustring (SpBic), spectral co-Clustring (SpCoc)), and found that the partitioning results obtained by Kmeans and GMM clustering algorithms were the best ( Figure 5 ).

[0053] Based on these test results, internal standards are required for calibration during mass spectrometry data acquisition, and background interference must be eliminated during cluster analysis. The optimal dimensionality reduction algorithm is factor analysis (FA), while clustering algorithms such as Gaussian mixture models (GMM) or K-means can be used. Therefore, this hybrid approach was used for data processing and analysis in subsequent analyses.

[0054] After Nano DESI mass spectrometry imaging is completed, dimensionality reduction and clustering are performed using the intensity matrix information to find the chemical structure subregions of the slice. However, one-step clustering analysis is usually difficult to obtain a good structural subregion, such as Figure 3 and Figure 4 As shown in Figure 2, although the number of clusters is set to 20, the clustering results can only be roughly divided into a few areas. The remaining dozen or so cluster results have few pixels and the regional distribution characteristics are not obvious. Obviously, several large distribution areas can be further divided. Therefore, we use the method of multiple continuous cluster analysis to Figure 6 The large area in d is continuously divided into small areas, and a finer structure is obtained ( Figure 6f) NanoDESI mass spectrometry imaging collects mass spectral data of analytes from sample slices. Continuous cluster analysis uses this mass spectral data to identify fine-scale structural subregions with distinct spatial chemical compositions, thereby capturing distinct chemical composition information from each subregion. Chain-ESI LESA allows for in situ collection of tandem mass spectral data for compounds from each subregion, enabling the identification of more metabolites.

[0055] Table 1 Mass spectrometry imaging results of rat brain slices (partial)

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] The present invention will be further described below with reference to specific embodiments.

[0065] Example 1

[0066] The brain is an important neural organ with many structural subregions. Based on the Brain Coordinate Framework, molecular feature subregions of rat brain slices were identified. Figure 2 a. Figure 2 b As can be seen, the chemical structure subregions obtained by continuous clustering are similar to the physiological structure subregions of the brain. Through the continuous clustering method, the brain can be partitioned from the perspective of chemical composition. The information of these chemical structure subregions is used to guide the Chain-ESI LESA extraction analysis, and the metabolite structure information can be obtained on the same slice (On tissue). The visualization results of different functional areas of the mouse brain and the collected tandem mass spectrometry data are shown in Figure 2. Figure 2c-2n, from the imaging results, the spatial distribution of amino acids, lipids and other substances in the brain can be intuitively observed, and some metabolites show a distribution that matches the physiological structure of the brain. For example, Taurinum is relatively abundant in the midbrain, thalamus, and hypothalamus; PC (20:2 / 18:3) and PC (12:0 / 22:1) are distributed throughout the brain, but are relatively rare in the corpus callosum, ventricle and other locations. The mass spectrometry imaging results of rat brain slices are shown in Table 1. Due to space reasons, this invention only shows part of the imaging results. The imaging results involved in the imaging results are shown in the figure. Figure 7 As shown in Table 1, Figure 7 Arranged in order from left to right.

[0067] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0068] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for multi-target molecular mass spectrometry imaging and in situ identification, characterized by: The following steps are involved: (1) placing the slice sample on an imaging analysis platform, and collecting primary mass spectrometry data of the slice sample in full scan mode using a mass spectrometry imaging module; (2) Performing continuous cluster analysis on the primary mass spectrometry data to obtain subregional distribution maps of molecular features with different composition; (3) Based on the molecular feature sub-region distribution map information, the liquid extraction surface analysis module is used to perform surface extraction on each region, and ionization is performed at the ion inlet of the mass spectrometer to collect tandem mass spectrometry data of the extract at the sample sampling point; (4) Accurate identification of imaging results is achieved based on the accurate mass information in the primary mass spectrometry data and the tandem mass spectrometry data.

2. The method for multi-target molecular mass spectrometry imaging and in situ identification according to claim 1, characterized in that: In step (1), the mass spectrometry imaging module uses a mass spectrometry imaging mode selected from the group consisting of Nano DESI, MALDIMSI, and SIMS MSI.

3. The method of multi-target molecular mass spectrometry imaging and in situ identification according to claim 1, characterized in that: In step (2), the cluster analysis includes a dimensionality reduction algorithm and a clustering algorithm.

4. The method for multi-target molecular mass spectrometry imaging and in situ identification according to claim 1, characterized in that: The dimensionality reduction algorithm is FA, and the clustering algorithm is Kmeans or GMM.

5. The method for multi-target molecular mass spectrometry imaging and in situ identification according to claim 1, characterized in that: In step (3), the electrospray ionization technology used in the liquid extraction surface analysis module includes but is not limited to any one of Chain ESI, Nano ESI and In ESI.

6. A device based on the multi-target molecular mass spectrometry imaging and in situ identification method according to any one of claims 1 to 5, characterized in that: The device includes a switching platform, on which an imaging device and a liquid extraction surface analysis module are provided. The imaging device includes a mass spectrometry imaging module, which includes a Venturi electrospray device. The Venturi electrospray device includes a solvent delivery pipeline, which is covered with a compressed gas pipeline, and an ionization voltage is applied to the solvent delivery pipeline. By introducing compressed gas, a negative pressure is formed at the front end of the nozzle of the solvent delivery pipeline, and the solution at the extraction point on the precision displacement platform is continuously extracted through the solvent delivery pipeline and ionized at the ion inlet of the mass spectrometer. The liquid extraction surface analysis module includes a liquid extraction surface analysis device, which drives the device to switch the carrier, and places the spray capillary on the liquid extraction surface analysis device perpendicular to the precision displacement platform. Under capillary action, the extraction solvent is sucked into the spray capillary, and the spray capillary is ionized at the ion inlet of the mass spectrometer by adjusting the driving device to switch the carrier.

7. The device for multi-target molecular mass spectrometry imaging and in situ identification according to claim 6, characterized in that: A liquid supply capillary is provided on the precision displacement platform, and a liquid bridge is formed between the solution delivery pipeline and the liquid supply capillary at a contact position.

8. The device for multi-target molecular mass spectrometry imaging and in situ identification according to claim 6, characterized in that: The liquid supply capillary is fixed on the No. 1 three-dimensional platform, the device switching platform is fixed on the No. 2 three-dimensional platform, the precision displacement platform and the No. 1 three-dimensional platform are fixed on the No. 3 three-dimensional platform, and the No. 3 three-dimensional platform is connected to the No. 2 three-dimensional platform.

Images