Ion mobility mass spectrum imaging data preprocessing method and device

Abstract

The invention discloses a preprocessing method and device for ion mobility mass spectrum imaging data, and the method comprises the steps: obtaining and analyzing the ion mobility mass spectrum imaging data of a to-be-detected tissue, and extracting the three-dimensional data of each pixel point; performing dimension reduction on three-dimensional data of each pixel point in the ion mobility mass spectrum imaging data to obtain two-dimensional data based on mass-to-charge ratio and intensity, and performing mass-to-charge ratio peak detection according to the two-dimensional data based on the mass-to-charge ratio and the intensity to obtain a mass-to-charge ratio peak; extracting the mass-to-charge ratio peak and the ion mobility in the front and back ranges of the mass-to-charge ratio peak from the three-dimensional data of each pixel point in the ion mobility mass spectrum imaging data to obtain two-dimensional data based on the ion mobility and the intensity; carrying out ion mobility peak detection according to the two-dimensional data based on the ion mobility and the intensity to obtain an ion mobility peak; and forming a two-dimensional peak by the mass-to-charge ratio peak and the ion mobility peak, and aligning and filtering to obtain a two-dimensional tissue characteristic peak and generate a corresponding molecular characteristic image. According to the invention, the accuracy of IM-MSI data analysis is improved.

Description

Technical Field

[0001] The present invention relates to the field of mass spectrometry imaging data processing, and in particular to a method and device for preprocessing ion mobility mass spectrometry imaging data. Background Art

[0002] Mass spectrometry imaging (MSI) is a powerful molecular imaging technique that can spatially localize a variety of biomolecules (such as lipids, peptides, proteins, and glycans) in biological tissues without the need for labeling. Ion mobility-mass spectrometry imaging (IM-MSI) is an advanced molecular imaging technique that combines two key molecular characteristics: mass-to-charge ratio (m / z) and collision cross section (CCS) to achieve high-precision spatial localization of biomolecules. Compared to traditional MSI techniques, IM-MSI infers the collision cross section (CCS) from ion mobility (1 / K0), providing additional structural information about ion structure, size, and charge state. Therefore, IM-MSI offers significant advantages in detecting low-abundance ions and distinguishing between isobaric ions or isomers, demonstrating greater application value in the analysis of complex biological tissues. Furthermore, IM-MSI accelerates data acquisition, enabling the precise identification and quantification of metabolites in biological tissues. In recent years, it has garnered widespread attention in fields such as biomedical research, drug development, and pathological analysis.

[0003] IM-MSI generates four-dimensional (4D) data, including spatial coordinates (x, y), m / z, and 1 / K0. Compared to traditional MSI data, IM-MSI data has a more complex structure and larger data volume. Data preprocessing is a key step in IM-MSI data analysis, aiming to extract molecular features from pixels and reconstruct molecular distribution maps of specific m / z and CCS values, providing a reliable basis for subsequent biological interpretation and data mining. However, existing IM-MSI data preprocessing methods have shortcomings in dealing with noise interference signals. Especially in the case of low signal-to-noise ratio (SNR), traditional local maximum methods have difficulty accurately identifying ion mobility peaks, resulting in reduced analysis accuracy and limiting data processing efficiency.

[0004] Although current commercial IM-MSI systems have integrated 4D data visualization tools, such as Bruker's SCiLS Lab, which allows users to manually set m / z values ​​and tolerance ranges to visualize molecular distributions. The MSiReader updated by Muddiman's team can read IM-MSI data matrices and manually extract specific m / z and ion mobility peaks. However, these tools still have limitations in high-throughput data processing and automated analysis. In practical applications, relying solely on manual operations and visualization tools is difficult to meet the needs of rapid analysis of large-scale, multi-dimensional data. Developing an efficient and automated IM-MSI data preprocessing method is crucial to improving 4D data analysis capabilities.

[0005] Currently, 4D data processing typically employs a dimensionality reduction strategy to reduce computational complexity and improve processing efficiency. A typical process involves feature extraction in the high-resolution m / z dimension, screening out high-confidence mass peaks, and then further extracting ion mobility peaks at the same m / z, thereby reducing the data dimension while retaining biological information to the greatest extent possible. However, the signal-to-noise ratio (SNR) of ion mobility signals is low, particularly for ion mobility signals corresponding to low-abundance m / z peaks. Therefore, an ideal preprocessing process should possess robust noise reduction capabilities, enabling accurate identification of low-abundance, low-intensity ion mobility signals to ensure the complete preservation of biological features. Summary of the Invention

[0006] The purpose of this application is to propose a preprocessing method and device for ion mobility mass spectrometry imaging data in response to the above-mentioned technical problems.

[0007] In a first aspect, the present invention provides a method for preprocessing ion mobility mass spectrometry imaging data, comprising the following steps:

[0008] Acquiring and analyzing ion mobility mass spectrometry imaging data of the tissue to be tested, and extracting three-dimensional data for each pixel in the ion mobility mass spectrometry imaging data, the three-dimensional data including mass-to-charge ratio, ion mobility, and intensity;

[0009] The three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data is reduced to two-dimensional data based on mass-to-charge ratio and intensity, and mass-to-charge ratio peak detection is performed based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak;

[0010] Extracting the mass-to-charge ratio peak and the ion mobility in the range before and after it from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; performing ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak;

[0011] The two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data are aligned and filtered to obtain two-dimensional tissue characteristic peaks; and the corresponding molecular characteristic image is generated based on each two-dimensional tissue characteristic peak.

[0012] Preferably, the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data is reduced to two-dimensional data based on mass-to-charge ratio and intensity, and mass-to-charge ratio peak identification is performed based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain the mass-to-charge ratio peak, specifically including:

[0013] Accumulate all intensities at the same mass-to-charge ratio in the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data, ignore ion mobility, and extract two-dimensional data based on mass-to-charge ratio and intensity;

[0014] The local intensity maximum method was used to identify the initial mass-to-charge ratio peak, and the median absolute deviation method was used to set the noise threshold. The initial mass-to-charge ratio peak was filtered based on the noise threshold to obtain the preliminarily filtered mass-to-charge ratio peak.

[0015] The mass-to-charge ratio peak spacing threshold is set, and the noise peak is filtered out of the mass-to-charge ratio peak after preliminary filtering using the mass-to-charge ratio peak spacing threshold to obtain the mass-to-charge ratio peak.

[0016] Preferably, performing ion mobility peak detection based on two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak specifically includes:

[0017] Calculate the density score for each ion mobility signal point in the two-dimensional data based on ion mobility and intensity as shown in the following formula:

[0018] Δmobility ij =|mobility i -mobility j |;

[0019]

[0020] Where i and j are the indices of the ion mobility signal points, respectively. j≠i means that the point itself is excluded from the density score calculation and only the surrounding ion mobility signal points are evaluated. N is the total number of ion mobility signal points, and Δmobility ij represents the ion mobility difference between ion mobility signal point i and ion mobility signal point j; Δmobility c represents the cutoff threshold for ion mobility differences, ρ i represents the density score of ion mobility signal point i;

[0021] The intensity score for each ion mobility signal point in the two-dimensional data based on ion mobility and intensity is calculated as follows:

[0022]

[0023] Among them, intensity i Indicates the intensity of the ion mobility signal point i, intensity j represents the intensity of the ion mobility signal at point j; δ i represents the intensity score of the ion mobility signal point i, and m is the number of points in the calculation of δ i The number of ion mobility signal points on each side of the ion mobility signal point i;

[0024] Selecting the ion mobility signal point with the lowest intensity and accounting for a threshold percentage of the total number of ion mobility signal points as the noise signal point, and extracting its density score and intensity score to obtain a density score set and an intensity score set of all noise signal points;

[0025] A density score threshold and an intensity score threshold are obtained based on the density score set and the intensity score set, respectively; ion mobility signal points with density scores lower than the density score threshold and intensity scores lower than the intensity score threshold are filtered to obtain ion mobility peaks.

[0026] Preferably, the density score threshold and the intensity score threshold are obtained based on the density score set and the intensity score set, respectively, specifically including:

[0027] The density score threshold and the intensity score threshold are obtained by averaging all density scores in the density score set and all intensity scores in the intensity score set, respectively, as shown in the following formula:

[0028] TOL ρ =mean(ρ noise );

[0029] TOL δ =mean(δ noise );

[0030] Among them, TOL ρ represents the density score threshold, ρ noise Represents the density score set of all noise signal points; TOL δ represents the intensity score threshold, δ noise Represents the strength score set of all noise signal points, and mean represents the average value.

[0031] Preferably, aligning and filtering the two-dimensional peaks of all pixels in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks specifically includes:

[0032] The two-dimensional peaks of all pixel points are grouped according to their mass-to-charge ratios. Starting with the mass-to-charge ratio peak with the maximum intensity, the other mass-to-charge ratio peaks are processed in sequence. The mass-to-charge ratio matching tolerance around the mass-to-charge ratio peak is set and the mass-to-charge ratio matching range is determined. The average mass-to-charge ratio of the mass-to-charge ratio peaks falling into the same mass-to-charge ratio matching range is used to replace the original mass-to-charge ratio to obtain the aligned mass-to-charge ratio peaks.

[0033] The two-dimensional peaks of all pixel points are grouped according to ion mobility, starting with the ion mobility peak with the maximum intensity, and the other ion mobility peaks are processed in sequence. The ion mobility matching tolerance around the ion mobility peak is set and the ion mobility matching range is determined. The average ion mobility of the ion mobility peaks falling within the same ion mobility matching range is used to replace the original ion mobility to obtain the aligned ion mobility peaks; the aligned mass-to-charge ratio peaks and the aligned ion mobility peaks constitute the aligned two-dimensional peaks;

[0034] The detection frequency of each aligned two-dimensional peak in all pixels is calculated, and the aligned two-dimensional peaks whose detection frequency is lower than the detection frequency threshold are filtered to obtain the two-dimensional tissue characteristic peaks.

[0035] Preferably, the molecular feature image can be used for tissue region segmentation, feature molecule screening, and potential biomarker identification.

[0036] In a second aspect, the present invention provides a preprocessing device for ion mobility mass spectrometry imaging data, comprising:

[0037] a data analysis module configured to acquire and analyze ion mobility mass spectrometry imaging data of the tissue to be tested, and extract three-dimensional data for each pixel in the ion mobility mass spectrometry imaging data, wherein the three-dimensional data includes mass-to-charge ratio, ion mobility, and intensity;

[0038] a mass-to-charge ratio peak detection module configured to reduce the dimensionality of the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, and perform mass-to-charge ratio peak detection based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak;

[0039] an ion mobility peak detection module configured to extract the mass-to-charge ratio peak and the ion mobility within the range before and after it from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; perform ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak;

[0040] The image generation module is configured to align and filter the two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks; and generate a corresponding molecular characteristic image based on each two-dimensional tissue characteristic peak.

[0041] In a third aspect, the present invention provides an electronic device comprising one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0043] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in any implementation manner in the first aspect when the computer program is executed by a processor.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The preprocessing method for ion mobility mass spectrometry imaging data proposed in the present invention performs efficient dimensionality reduction and peak screening on ion mobility mass spectrometry imaging data. First, high-confidence mass-to-charge ratio peaks are extracted in the m / z dimension, and then ion mobility peaks are extracted within the corresponding m / z tolerance range. The peak detection results are further optimized through two-dimensional peak alignment and peak filtering steps. Combined with the peak detection frequency screening mechanism, random noise peaks and low-frequency peaks are effectively removed, making the detected molecular features more stable and suitable for high-throughput analysis of complex biological tissues. While reducing data complexity, this method retains the integrity of molecular features, improves computational efficiency, and ensures the accuracy of peak matching.

[0046] (2) The preprocessing method for ion mobility mass spectrometry imaging data proposed in the present invention introduces a peak detection strategy based on density clustering in the ion mobility dimension. By calculating the density score-intensity score, the peak detection accuracy under low signal-to-noise ratio conditions is improved, noise interference is effectively removed, and the ability to extract low-abundance ion signals is improved, ensuring high-confidence analysis of ion mobility mass spectrometry imaging data.

[0047] (3) The preprocessing method for ion mobility mass spectrometry imaging data proposed in the present invention overcomes the limitations of existing technologies in low signal-to-noise ratio ion mobility peak detection, improves the accuracy of IM-MSI data analysis, and supports comprehensive molecular feature analysis of complex biological systems; it also has a wide range of applicability, not only suitable for different types of ion mobility mass spectrometry imaging data, including ion mobility mass spectrometry imaging data collected by MALDI-TIMS and ESI-TIMS, but can also be extended to other high-dimensional imaging data analysis, such as spatial metabolomics, spatial proteomics and spatial transcriptomics research, providing reliable data support for multi-omics integrated analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 Schematic diagram of a process for preprocessing ion mobility mass spectrometry imaging data according to an embodiment of the present application;

[0050] Figure 2 This is a framework diagram of a method for preprocessing ion mobility mass spectrometry imaging data according to an embodiment of the present application;

[0051] Figure 3 The clustering results of mouse larval tissue samples in the embodiments of the present application;

[0052] Figure 4 Screening for potential molecular markers in specific regions of mouse juvenile tissue samples according to the embodiments of the present application;

[0053] Figure 5 Schematic diagram of a preprocessing device for ion mobility mass spectrometry imaging data according to an embodiment of the present application;

[0054] Figure 6 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0056] Figure 1 A method for preprocessing ion mobility mass spectrometry imaging data provided in an embodiment of the present application is shown, comprising the following steps:

[0057] S1, obtaining and analyzing ion mobility mass spectrometry imaging data of the tissue to be tested, and extracting three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data, the three-dimensional data including mass-to-charge ratio, ion mobility and intensity.

[0058] Specifically, refer to Figure 2The specific process for parsing ion mobility mass spectrometry imaging data in the embodiments of the present application is as follows: The raw ion mobility mass spectrometry imaging (IM-MSI) data in .d format is parsed to extract the three-dimensional data of each pixel, including mass-to-charge ratio (m / z), ion mobility (1 / K0), and intensity (Intensity). The IM-MSI data is parsed using the Python script timspy package, and data with columns named 'frame', 'intensity', 'mz', and 'inv_ion_mobility' are extracted. The data for each pixel is saved separately in .csv format to improve the computational efficiency of data processing and meet the needs of high-throughput data analysis.

[0059] S2, reducing the dimensionality of the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, performing mass-to-charge ratio peak detection based on the two-dimensional data based on mass-to-charge ratio and intensity, and obtaining a mass-to-charge ratio peak.

[0060] In a specific embodiment, the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data is reduced to two-dimensional data based on mass-to-charge ratio and intensity, and mass-to-charge ratio peak identification is performed based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain the mass-to-charge ratio peak, specifically including:

[0061] Accumulate all intensities at the same mass-to-charge ratio in the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data, ignore ion mobility, and extract two-dimensional data based on mass-to-charge ratio and intensity;

[0062] The local intensity maximum method was used to identify the initial mass-to-charge ratio peak, and the median absolute deviation method was used to set the noise threshold. The initial mass-to-charge ratio peak was filtered based on the noise threshold to obtain the preliminarily filtered mass-to-charge ratio peak.

[0063] The mass-to-charge ratio peak spacing threshold is set, and the noise peak is filtered out of the mass-to-charge ratio peak after preliminary filtering using the mass-to-charge ratio peak spacing threshold to obtain the mass-to-charge ratio peak.

[0064] Specifically, the embodiment of the present application first reduces dimension to two-dimensional (2D) data (comprising m / z and Intensity) by three-dimensional data, to reduce data dimension and reduce computational complexity, adopts local intensity maximum method to identify initial mass-to-charge ratio peak, identifies the peak value that local intensity is outstanding, to ensure that the mass-to-charge ratio peak detected has significant intensity characteristics;Noise threshold is set with median absolute deviation (MAD) method, filters the mass-to-charge ratio peak below noise threshold, to reduce noise interference and retain the mass-to-charge ratio peak with biological significance. In one of embodiment, setting mass-to-charge ratio peak spacing threshold is at least 20ppm, to reduce pseudo-peak interference, improve the accuracy of peak detection. The setting of this mass-to-charge ratio peak spacing threshold helps to reduce the influence of noise peak, improves the stability of peak detection, while avoiding the peak aliasing problem caused by mass resolution limitation, ensures that peak value can accurately match in subsequent data processing. In addition, by reasonable peak spacing limit, it is also possible to optimize computational efficiency, reduce redundant calculations, further accelerate data processing speed, thereby improving the accuracy and processing performance of overall data analysis.

[0065] S3, extracting the mass-to-charge ratio peak and the ion mobility in the range before and after it from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; performing ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak.

[0066] In a specific embodiment, performing ion mobility peak detection based on two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak specifically includes:

[0067] Calculate the density score for each ion mobility signal point in the two-dimensional data based on ion mobility and intensity as shown in the following formula:

[0068] Δmobility ij =|mobility i -mobility j |;

[0069]

[0070] Where i and j are the indices of the ion mobility signal points, respectively. j≠i means that the point itself is excluded from the density score calculation and only the surrounding ion mobility signal points are evaluated. N is the total number of ion mobility signal points, and Δmobility ij represents the ion mobility difference between ion mobility signal point i and ion mobility signal point j; Δmobility c represents the cutoff threshold for ion mobility differences, ρ i represents the density score of ion mobility signal point i;

[0071] The intensity score for each ion mobility signal point in the two-dimensional data based on ion mobility and intensity is calculated as follows:

[0072]

[0073] Among them, intensity i Indicates the intensity of the ion mobility signal point i, intensity j represents the intensity of the ion mobility signal at point j; δ i represents the intensity score of the ion mobility signal point i, and m is the number of points in the calculation of δ i The number of ion mobility signal points on each side of the ion mobility signal point i;

[0074] Selecting the ion mobility signal point with the lowest intensity and accounting for a threshold percentage of the total number of ion mobility signal points as the noise signal point, and extracting its density score and intensity score to obtain a density score set and an intensity score set of all noise signal points;

[0075] A density score threshold and an intensity score threshold are obtained based on the density score set and the intensity score set, respectively; ion mobility signal points with density scores lower than the density score threshold and intensity scores lower than the intensity score threshold are filtered to obtain ion mobility peaks.

[0076] In a specific embodiment, the density score threshold and the intensity score threshold are obtained based on the density score set and the intensity score set, respectively, including:

[0077] The density score threshold and the intensity score threshold are obtained by averaging all density scores in the density score set and all intensity scores in the intensity score set, respectively, as shown in the following formula:

[0078] TOL ρ =mean(ρ noise );

[0079] TOL δ =mean(δ noise );

[0080] Among them, TOL ρ represents the density score threshold, ρ noise Represents the density score set of all noise signal points; TOL δ represents the intensity score threshold, δ noise Represents the strength score set of all noise signal points, and mean represents the average value.

[0081] Specifically, the embodiments of the present application construct an ion mobility peak detection method based on density clustering, calculate density scores and intensity scores, and screen high-confidence ion mobility peaks to ensure accurate extraction of characteristic ion signals. In one embodiment, ion mobility is extracted within the detected mass-to-charge ratio peak and its front and back range (±20ppm), and 2D data (including 1 / K0 and Intensity) is obtained, and then the density score (ρ) of each ion mobility signal point is calculated. In the calculation formula of the density score, the truncation threshold of the ion mobility difference is usually set to cover the number of "neighboring signals" accounting for about 10% of the total number N; the density score indicates that the closer the ion mobility signal point is to the ion mobility signal point i, the higher the contribution. The intensity score (δ) of the ion mobility signal point is further calculated. In the calculation formula of the intensity score, m defaults to 30, δ i δ is a measure of the intensity difference of the ion mobility signal point i relative to other ion mobility signal points in the local area. Only when the ion mobility signal point i has the highest signal intensity in its local area, δ i is non-zero to effectively screen out significant signals.

[0082] In the process of ion mobility peak detection, to ensure the reliability of the peak, a density score threshold and an intensity score threshold are set to exclude low-confidence signals. In one embodiment, the 5% of ion mobility signal points with the lowest signal intensity are selected as noise signal points, and their density scores and intensity scores are extracted. The average is then calculated and used as the TOL. ρ and TOL ρ This strategy can effectively exclude low-intensity or low-density pseudo signals and improve the accuracy and robustness of peak detection.

[0083] S4, aligning and filtering the two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks; and generating a corresponding molecular characteristic image based on each two-dimensional tissue characteristic peak.

[0084] In a specific embodiment, aligning and filtering the two-dimensional peaks of all pixels in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks specifically includes:

[0085] The two-dimensional peaks of all pixel points are grouped according to their mass-to-charge ratios. Starting with the mass-to-charge ratio peak with the maximum intensity, the other mass-to-charge ratio peaks are processed in sequence. The mass-to-charge ratio matching tolerance around the mass-to-charge ratio peak is set and the mass-to-charge ratio matching range is determined. The average mass-to-charge ratio of the mass-to-charge ratio peaks falling into the same mass-to-charge ratio matching range is used to replace the original mass-to-charge ratio to obtain the aligned mass-to-charge ratio peaks.

[0086] The two-dimensional peaks of all pixel points are grouped according to ion mobility, starting with the ion mobility peak with the maximum intensity, and the other ion mobility peaks are processed in sequence. The ion mobility matching tolerance around the ion mobility peak is set and the ion mobility matching range is determined. The average ion mobility of the ion mobility peaks falling within the same ion mobility matching range is used to replace the original ion mobility to obtain the aligned ion mobility peaks; the aligned mass-to-charge ratio peaks and the aligned ion mobility peaks constitute the aligned two-dimensional peaks;

[0087] The detection frequency of each aligned two-dimensional peak in all pixels is calculated, and the aligned two-dimensional peaks whose detection frequency is lower than the detection frequency threshold are filtered to obtain the two-dimensional tissue characteristic peaks.

[0088] Specifically, the two-dimensional peaks of all pixel points (including mass-to-charge ratio peaks and ion mobility peaks) are grouped, matched, and filtered to eliminate low-frequency noise peaks and improve the stability and consistency of peak detection, as follows:

[0089] S41, the two-dimensional peaks detected in all pixels are grouped according to m / z, and mass-to-charge ratio peak alignment is performed. Starting with the mass-to-charge ratio peak of the highest intensity, other mass-to-charge ratio peaks are processed in sequence. In one embodiment, the mass-to-charge ratio matching tolerance is set to 20ppm, and the mass-to-charge ratio matching range is determined to be the range within the mass-to-charge ratio matching tolerance of the mass-to-charge ratio peak and its surroundings. In the matching process, if multiple mass-to-charge ratio peaks fall into the same mass-to-charge ratio matching range, the original m / z value is replaced by the average m / z value of these mass-to-charge ratio peaks to reduce mass drift error and improve data consistency.

[0090] S42: Group the two-dimensional peaks detected in all pixels by ion mobility and perform ion mobility peak alignment. Starting with the ion mobility peak with the highest intensity, the remaining peaks are processed sequentially. In one embodiment, the ion mobility matching tolerance is set to 0.5%, and the ion mobility matching range is determined to be the range within the ion mobility matching tolerance of the ion mobility peak and its surroundings. During the matching process, if multiple ion mobility peaks fall within the same ion mobility matching range, the average ion mobility value of these ion mobility peaks is used to replace the original ion mobility value to reduce drift error and ensure ion mobility peak alignment accuracy and data consistency.

[0091] S43, calculate the peak frequency to screen stable peaks. Calculate the detection frequency of each aligned two-dimensional peak in all pixel points, that is, count its proportion in all pixel points. In one embodiment, the detection frequency threshold is set to 5%, and the aligned two-dimensional peaks with a detection frequency lower than the detection frequency threshold are regarded as noise peaks and filtered to exclude low-frequency interference signals caused by random noise or instrument drift. The aligned two-dimensional peaks with a detection frequency higher than 5% are regarded as tissue characteristic peaks, which can be used for subsequent molecular imaging reconstruction and biomarker analysis. This step effectively enhances the stability and biological relevance of peak detection, and provides high-quality peak data support for subsequent analysis.

[0092] After the above processing, the two-dimensional tissue characteristic peaks present in all pixel points are output, the molecular characteristic information with high confidence is retained, and a molecular characteristic image is generated. In one embodiment, 1261 two-dimensional tissue characteristic peaks are screened, that is, the total number of mass-to-charge ratio peaks and ion mobility peaks after alignment and screening is 1261, and the characteristic matrix M can be further constructed. 255×135×1261 , to generate molecular feature images. 1261 two-dimensional tissue feature peaks correspond to 1261 molecular feature images, which constitute the image set M0 = {m p ,p=1,2,...,1261},m p It represents the molecular feature image corresponding to the p-th two-dimensional tissue characteristic peak, and each molecular feature image has 255×135 pixels.

[0093] In a specific embodiment, the molecular signature image can be used for tissue region segmentation, signature molecule screening, and potential biomarker identification.

[0094] Specifically, the molecular feature images extracted by the preprocessing method of ion mobility mass spectrometry imaging data proposed in the embodiment of the present application can be further used for downstream tasks such as tissue region segmentation, characteristic molecule screening, and potential biomarker identification. Taking the tissue partitioning task as an example, the Python script UMAP package is used to reduce the dimension of the above image set to obtain a low-dimensional embedded IM-MSI data matrix E 255×135×3 , that is, the embedded ion image set E0 = {e q ,q=1,2,3}, where the algorithm's hyperparameters are metric='cosine', n_neighbors=100, and n_components=3. Then, the K-means clustering method (n_clusters=20) in the Python script Scikit-learn package was used to segment E0 into background and tissue regions, thus obtaining tissue partitioning results. Therefore, the molecular feature images can be used for biological tissue clustering analysis, colocalization analysis, and characteristic molecule screening, further supporting disease mechanism research and metabolic feature analysis.

[0095] According to an embodiment of the present application, IM-MSI data of mouse juvenile tissues were processed. Figure 3 Clustering results for IM-MSI data from mouse juvenile tissue are presented, validating the effectiveness of this preprocessing method in practical downstream tasks. The results demonstrate that, through UMAP dimensionality reduction and K-Means cluster analysis, the major anatomical structures of the tissue, including the brain, spinal cord, liver, lungs, and heart, can be clearly segmented, highly consistent with histological images. This demonstrates that this method effectively extracts and preserves spatial information in IM-MSI data, improving the accuracy of tissue segmentation.

[0096] On this basis, in order to further explore the metabolic characteristics of different tissue regions, co-localization analysis was used to screen molecular markers related to specific organ tissues, such as Figure 4 As shown. The study found that there are significant co-localized ion clusters in specific organ tissue regions. These ion clusters may be biologically related to specific metabolic pathways or functional activities. For example, specific ions in the liver region may be related to bile acid metabolism, while labeled ions in the brain region may be involved in the metabolic process of neurotransmitters. In addition, the distribution of characteristic ions in different tissue regions in pixel space was calculated and its consistency with tissue anatomical divisions was evaluated, further demonstrating the application value of this method in tissue-specific molecular analysis.

[0097] Overall, the processing method proposed in the embodiments of this application can not only be used for subsequent bioinformatics analysis, such as tissue region division, characteristic molecule screening, and potential biomarker identification, but also provide high-precision and reliable data support for molecular imaging research of complex biological systems, helping to deeply explore the molecular mechanisms of tissue microenvironment.

[0098] The embodiments of the present application process ion mobility mass spectrometry imaging data, effectively integrating key information from the ion mobility mass spectrometry imaging data while reducing the data size and improving computational efficiency. Furthermore, the method can also identify local molecular features within different tissue regions, providing more accurate spatial distribution information.

[0099] It is worth mentioning that the preprocessing method for ion mobility mass spectrometry imaging data proposed in an embodiment of the present invention is not only applicable to the analysis of MALDI-TIMS data, but also applicable to ion mobility mass spectrometry imaging data in other formats, which is not limited in this embodiment.

[0100] Further references Figure 5 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a preprocessing device for ion mobility mass spectrometry imaging data. Figure 1Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0101] The present invention provides a device for preprocessing ion mobility mass spectrometry imaging data, comprising:

[0102] Data parsing module 1 is configured to acquire and parse ion mobility mass spectrometry imaging data of the tissue to be tested, extracting three-dimensional data for each pixel in the ion mobility mass spectrometry imaging data, the three-dimensional data including mass-to-charge ratio, ion mobility, and intensity;

[0103] The mass-to-charge ratio peak detection module 2 is configured to reduce the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, and perform mass-to-charge ratio peak detection based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak;

[0104] an ion mobility peak detection module 3 configured to extract the mass-to-charge ratio peak and the ion mobility in the range before and after it from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; perform ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak;

[0105] The image generation module 4 is configured to align and filter the two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks; and generate a corresponding molecular characteristic image based on each two-dimensional tissue characteristic peak.

[0106] Figure 6 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. Figure 6 As shown, the electronic device of this embodiment includes: a processor 601 and a memory 602; wherein the memory 602 is used to store computer-executable instructions; and the processor 601 is used to execute the computer-executable instructions stored in the memory to implement the various steps performed by the electronic device in the above embodiment. For details, please refer to the relevant description of the above method embodiment.

[0107] Optionally, the memory 602 may be independent or integrated with the processor 601 .

[0108] When the memory 602 is independently provided, the electronic device further includes a bus 603 for connecting the memory 602 and the processor 601 .

[0109] An embodiment of the present invention further provides a computer storage medium, in which computer execution instructions are stored. When the processor 601 executes the computer execution instructions, the above method is implemented.

[0110] An embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by the processor 601, the above method is implemented.

[0111] In the embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or module, which may be electrical, mechanical or other forms.

[0112] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to implement the solution of this embodiment based on actual needs.

[0113] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The units formed by the above modules may be implemented in the form of hardware or hardware plus software functional units.

[0114] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or processor 601 to perform some steps of the methods of various embodiments of the present application.

[0115] It should be understood that the processor 601 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASIC). A general-purpose processor may be a microprocessor, or the processor 601 may be any conventional processor 601. The steps of the method disclosed in the present invention may be directly implemented by the hardware processor 601, or implemented by a combination of hardware and software modules in the processor 601.

[0116] The memory 602 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

[0117] Bus 603 can be an Industry Standard Architecture (ISA), a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Bus 603 can be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the bus 603 in the drawings of this application is not limited to a single bus 603 or a single type of bus 603.

[0118] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0119] An exemplary storage medium is coupled to the processor 601, so that the processor 601 can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor 601. The processor 601 and the storage medium can be located in an application-specific integrated circuit (ASIC). Of course, the processor 601 and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0120] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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. A method for preprocessing ion mobility mass spectrometry imaging data, characterized in that: The following steps are involved: Acquiring and analyzing ion mobility mass spectrometry imaging data of the tissue to be tested, and extracting three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data, wherein the three-dimensional data includes mass-to-charge ratio, ion mobility, and intensity; reducing the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, and performing mass-to-charge ratio peak detection based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak; Extracting the mass-to-charge ratio peak and the ion mobility in the range before and after it from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; performing ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak; The two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data are aligned and filtered to obtain two-dimensional tissue characteristic peaks; and a corresponding molecular characteristic image is generated based on each two-dimensional tissue characteristic peak.

2. The method for preprocessing ion mobility mass spectrometry imaging data according to claim 1, characterized in that: Reducing the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, and performing mass-to-charge ratio peak identification based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak, specifically comprising: Accumulating all intensities at the same mass-to-charge ratio in the three-dimensional data of each pixel in the ion mobility mass spectrometry imaging data, ignoring ion mobility, and extracting two-dimensional data based on mass-to-charge ratio and intensity; The initial mass-to-charge ratio peak is identified by using the local intensity maximum method, the noise threshold is set by using the median absolute deviation method, and the initial mass-to-charge ratio peak is filtered based on the noise threshold to obtain a preliminarily filtered mass-to-charge ratio peak; A mass-to-charge ratio peak spacing threshold is set, and noise peak filtering is performed on the mass-to-charge ratio peak after the preliminary filtering using the mass-to-charge ratio peak spacing threshold to obtain a mass-to-charge ratio peak.

3. The method for preprocessing ion mobility mass spectrometry imaging data according to claim 1, characterized in that: Performing ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak specifically includes: The density score of each ion mobility signal point in the two-dimensional data based on ion mobility and intensity is calculated as shown in the following formula: Δmobility ij =|mobility i -mobility j |; Where i and j are the indices of the ion mobility signal points, respectively. j≠i means that the point itself is excluded from the density score calculation and only the surrounding ion mobility signal points are evaluated. N is the total number of ion mobility signal points, and Δmobility ij represents the ion mobility difference between ion mobility signal point i and ion mobility signal point j; Δmobility c represents the cutoff threshold for ion mobility differences, ρ i represents the density score of ion mobility signal point i; The intensity score of each ion mobility signal point in the two-dimensional data based on ion mobility and intensity is calculated as shown in the following formula: Among them, intensity i Indicates the intensity of the ion mobility signal point i, intensity j represents the intensity of the ion mobility signal at point j; δ i represents the intensity score of the ion mobility signal point i, and m is the number of points in the calculation of δ i The number of ion mobility signal points on each side of the ion mobility signal point i; Selecting the ion mobility signal point with the lowest intensity and accounting for a threshold percentage of the total number of ion mobility signal points as the noise signal point, and extracting its density score and intensity score to obtain a density score set and an intensity score set of all noise signal points; A density score threshold and an intensity score threshold are obtained based on the density score set and the intensity score set, respectively; and ion mobility signal points whose density scores are lower than the density score threshold and whose intensity scores are lower than the intensity score threshold are filtered to obtain ion mobility peaks.

4. The method for preprocessing ion mobility mass spectrometry imaging data according to claim 3, characterized in that: The density score threshold and the intensity score threshold are obtained based on the density score set and the intensity score set, respectively, specifically including: The density score threshold and the intensity score threshold are obtained by averaging all density scores in the density score set and all intensity scores in the intensity score set, respectively, as shown in the following formula: TOL ρ =mean(ρ noise ); TOL δ =mean(δ noise ); Among them, TOL ρ represents the density score threshold, ρ noise Represents the density score set of all noise signal points; TOL δ represents the intensity score threshold, δ noise Represents the strength score set of all noise signal points, and mean represents the average value.

5. The method for preprocessing ion mobility mass spectrometry imaging data according to claim 1, characterized in that: Aligning and filtering the two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks, specifically including: The two-dimensional peaks of all pixel points are grouped according to their mass-to-charge ratios. Starting with the mass-to-charge ratio peak with the maximum intensity, the other mass-to-charge ratio peaks are processed in sequence. The mass-to-charge ratio matching tolerance around the mass-to-charge ratio peak is set and the mass-to-charge ratio matching range is determined. The average mass-to-charge ratio of the mass-to-charge ratio peaks falling into the same mass-to-charge ratio matching range is used to replace the original mass-to-charge ratio to obtain the aligned mass-to-charge ratio peaks. Grouping the two-dimensional peaks of all pixel points according to ion mobility, starting with the ion mobility peak with the maximum intensity, processing the other ion mobility peaks in sequence, setting the ion mobility matching tolerance around the ion mobility peak and determining the ion mobility matching range, replacing the original ion mobility with the average ion mobility of the ion mobility peaks falling within the same ion mobility matching range, to obtain aligned ion mobility peaks; the aligned mass-to-charge ratio peaks and the aligned ion mobility peaks constitute the aligned two-dimensional peaks; The detection frequency of each aligned two-dimensional peak in all pixel points is calculated, and the aligned two-dimensional peaks whose detection frequency is lower than the detection frequency threshold are filtered to obtain the two-dimensional tissue characteristic peaks.

6. The method for preprocessing ion mobility mass spectrometry imaging data according to claim 1, characterized in that: The molecular characteristic image can be used for tissue region division, characteristic molecule screening, and potential biomarker identification.

7. A preprocessing device for ion mobility mass spectrometry imaging data, characterized in that: include: a data analysis module configured to acquire and analyze ion mobility mass spectrometry imaging data of the tissue to be tested, and extract three-dimensional data for each pixel in the ion mobility mass spectrometry imaging data, wherein the three-dimensional data includes mass-to-charge ratio, ion mobility, and intensity; a mass-to-charge ratio peak detection module, configured to reduce the dimensionality of the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data into two-dimensional data based on mass-to-charge ratio and intensity, and perform mass-to-charge ratio peak detection based on the two-dimensional data based on mass-to-charge ratio and intensity to obtain a mass-to-charge ratio peak; an ion mobility peak detection module configured to extract the mass-to-charge ratio peak and the ion mobility within its front and rear range from the three-dimensional data of each pixel point in the ion mobility mass spectrometry imaging data to obtain two-dimensional data based on ion mobility and intensity; perform ion mobility peak detection based on the two-dimensional data based on ion mobility and intensity to obtain an ion mobility peak; the mass-to-charge ratio peak and the ion mobility peak constitute a two-dimensional peak; An image generation module is configured to align and filter the two-dimensional peaks of all pixel points in the ion mobility mass spectrometry imaging data to obtain two-dimensional tissue characteristic peaks; A corresponding molecular feature image is generated based on each two-dimensional tissue feature peak.

8. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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