Mass spectrometry based processing method and system for gastrointestinal tissue

Abstract

The application provides a mass spectrum processing method and system based on digestive tract tissues, and is applied to the technical field of mass spectrum data processing. By acquiring mass spectrum of each digestive tract tissue; the retention time and mass-to-charge ratio information of each main peak in the mass spectrum are automatically extracted to obtain a core feature vector representing the protein abundance of the digestive tract tissue; and the protein identity information associated with the index of the core feature vector is determined; according to the core feature vector of the healthy digestive tract tissue, a first protein abundance is determined; and according to the core feature vector of each of the multiple diseased digestive tract tissues, multiple second protein abundances are determined; the fold change and the significant difference value are calculated according to the first protein abundance and the multiple second protein abundances; the target protein abundance corresponding to the fold change and the significant difference value satisfying the preset condition is determined; and according to the protein identity information corresponding to the target protein abundance, a protein differential quantification list is output. It has high credibility and data traceability.

Description

Technical Field

[0001] This application relates to the field of mass spectrometry data processing technology, and in particular to a method and system for processing mass spectra based on digestive tract tissue. Background Technology

[0002] In proteomics studies based on digestive tract tissues, obtaining accurate differentially expressed protein lists is crucial for revealing tissue biological characteristics and discovering key biomarkers. However, due to the unique physicochemical properties of digestive tract tissues, existing processing methods still have the following limitations in generating differentially expressed protein lists (hereinafter referred to as "lists"): First, digestive tract tissues exhibit extremely high sample heterogeneity. These tissues have complex physiological structures and are rich in endogenous proteases and mucins. Conventional sample processing methods struggle to effectively inhibit enzyme activity immediately upon ex vivo, and residual enzymes readily lead to non-specific degradation of target proteins. This results in lists that fail to accurately reflect the protein composition state of the tissue at the time of ex vivo examination. Simultaneously, strong background interference in the tissue matrix often masks low-abundance signals of research value, leading to missing crucial information in the lists. Second, existing differential protein quantification lists lack accuracy. Because protein expression fluctuations between different states of the same type of tissue are often very subtle (e.g., on the order of 1.2), traditional label-free quantification or simple proportional analysis methods not only fail to eliminate batch-based systematic errors in sample preparation and testing but also generally lack in-depth statistical evaluation of the distribution data of repeated samples. This makes the selected lists highly susceptible to interference from experimental environments or individualized random fluctuations, resulting in a high false positive rate and poor reliability and scientific rigor.

[0003] Therefore, how to output a more accurate list of protein differential quantifications has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] This application provides a method and system for mass spectrometry processing based on digestive tract tissues, which addresses the shortcomings of existing technologies such as severe interference from endogenous enzymes in digestive tract tissues, high background heterogeneous impurities, and low accuracy of protein differential quantification lists. It achieves true mapping of protein components in isolated tissues and high-confidence capture of weak expression differences, thereby outputting a highly accurate protein differential quantification list.

[0005] This application provides a method for mass spectrometry processing based on digestive tract tissue, including: Obtain the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues; For each mass spectrum, the retention time and mass-to-charge ratio information corresponding to each major peak in the mass spectrum are automatically extracted to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; and the protein identity information associated with the core feature vector index is determined. Based on the core feature vector corresponding to the healthy digestive tract tissue, a first protein abundance is determined; and based on the core feature vectors of the multiple diseased digestive tract tissues, multiple second protein abundances are determined. Calculate the fold difference of the plurality of second protein abundances relative to the first protein abundance, and the significance value of each of the plurality of second protein abundances; From the plurality of second protein abundances, determine the target protein abundance corresponding to the difference fold and significant difference value that meet the preset conditions; and output a protein difference quantification list based on the protein identity information corresponding to the target protein abundance.

[0006] According to an embodiment of this application, a method for processing mass spectra of digestive tract tissues includes obtaining mass spectra of multiple isolated digestive tract tissues. The method comprises: preprocessing and quantifying proteins in each isolated digestive tract tissue to obtain quantified proteins; performing FASP enzymatic digestion on the quantified proteins to obtain multiple first peptides; specifically labeling the multiple first peptides with isotope tags to obtain multiple second peptides, each carrying a mass difference signal to be detected; mixing the multiple second peptides corresponding to each of the multiple digestive tract tissues to obtain mixed peptides; performing liquid chromatography fractionation on the mixed peptides using high-pH reversed-phase chromatography to obtain multiple component third peptides; performing liquid chromatography-mass spectrometry analysis on the third peptides and capturing the reporter ion intensities corresponding to each isotope tag in the third peptides in real time; and resolving the reporter ion intensities to obtain the mass spectra of each of the multiple digestive tract tissues.

[0007] According to an embodiment of this application, a mass spectrometry processing method based on digestive tract tissue is provided. The method involves preprocessing the isolated digestive tract tissue and quantifying proteins to obtain quantified proteins. The method includes: adding SDT lysis buffer to the isolated digestive tract tissue and homogenizing it using a homogenizer to obtain tissue lysis buffer; subjecting the tissue lysis buffer to a boiling water bath to inactivate endogenous proteases in the tissue lysis buffer to obtain denatured lysis buffer; centrifuging the denatured lysis buffer and collecting the supernatant; and quantifying the proteins in the supernatant using the dioctanine BCA method to obtain the quantified proteins.

[0008] According to an embodiment of this application, a method for processing mass spectrometry data based on digestive tract tissue includes determining the protein identity information associated with the core feature vector index. This includes: acquiring a protein sequence database corresponding to the digestive tract tissue represented by the mass spectrometry data; simulating the FASP enzymatic digestion rules and isotopic label modification rules for filter-assisted sample preparation to generate a theoretical peptide library with theoretical mass information; comparing the mass-to-charge ratio information of secondary fragment ions in the core feature vector with the theoretical fragment ions in the theoretical peptide library to calculate the association score for each matching entry; filtering the association scores based on preset primary and secondary mass tolerances to lock in candidate peptide sequences corresponding to the core feature vector; and mapping the candidate peptide sequences to corresponding proteins when the false positive rate of the candidate peptide sequences is less than a preset threshold to determine the protein identity information associated with the core feature vector index.

[0009] According to an embodiment of this application, a method for processing mass spectrometry data based on digestive tract tissue is provided. The step of determining the first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue includes: extracting reporter ion intensities within a preset mass-to-charge ratio range from the core feature vector corresponding to the healthy digestive tract tissue; summing and aggregating the reporter ion intensities of all peptides belonging to the same protein based on the retention time and peptide sequence information in the reporter ion intensities to obtain the original abundance value for the protein dimension; and performing median normalization on the original abundance value to align the signal references between different isotope channels to obtain the first protein abundance.

[0010] According to an embodiment of this application, a method for processing mass spectrometry data based on digestive tract tissue is provided. The step of calculating the fold difference between the abundance of the plurality of second proteins and the abundance of the first protein includes: performing the following operations for each second protein abundance: using the ratio of the second protein abundance to the first protein abundance as the original fold difference; performing a base-2 logarithmic transformation on the original fold difference to obtain a logarithmic difference value; and using the logarithmic difference value as the fold difference between the second protein abundance and the first protein abundance.

[0011] According to an embodiment of this application, a method for processing mass spectrometry data based on digestive tract tissue includes calculating the significant difference value of each of the plurality of second protein abundances. This includes: obtaining a first replicate sample corresponding to the healthy digestive tract tissue and obtaining first abundance distribution data corresponding to the first replicate sample; for each second protein abundance, obtaining a second replicate sample corresponding to the diseased digestive tract tissue corresponding to the second protein abundance and obtaining second abundance distribution data corresponding to the second replicate sample; and using the statistical difference between the second abundance distribution data and the first abundance distribution data as the significant difference value of the second protein abundance.

[0012] According to an embodiment of this application, a method for processing mass spectrometry data based on digestive tract tissue includes: determining the target protein abundance from a plurality of second protein abundances that meets preset conditions for fold change and significant difference value; and outputting a protein difference quantification list based on the protein identity information corresponding to the target protein abundance. This includes: selecting the second protein abundance from the plurality of second protein abundances that has a fold change within a preset range and a significant difference value less than a preset difference threshold as the target protein abundance; determining the target core feature vector corresponding to the target protein abundance; and outputting a protein difference quantification list containing a protein access number, protein name, fold change, and significant difference value based on the protein identity information associated with the target core feature vector index.

[0013] This application also provides a mass spectrometry processing system based on digestive tract tissue, including: The acquisition module is used to acquire the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues. The processing module is used to automatically extract the retention time and mass-to-charge ratio information corresponding to each major peak in each mass spectrum to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; determine the protein identity information associated with the core feature vector index; determine a first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue; determine multiple second protein abundances based on the core feature vectors of the multiple diseased digestive tract tissues; calculate the fold difference of the multiple second protein abundances relative to the first protein abundance, and the significant difference value of each of the multiple second protein abundances; determine the target protein abundance corresponding to the fold difference and significant difference value that meet preset conditions from the multiple second protein abundances; and output a protein differential quantification list based on the protein identity information corresponding to the target protein abundance.

[0014] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the mass spectrometry processing method based on digestive tract tissue as described above.

[0015] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the mass spectrometry processing method based on digestive tract tissue as described above.

[0016] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the mass spectrometry processing method based on digestive tract tissue as described above.

[0017] The mass spectrometry processing method and system based on digestive tract tissue provided in this application acquires mass spectra of multiple ex vivo digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues. For each mass spectra, the method automatically extracts the retention time and mass-to-charge ratio information corresponding to each major peak to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue. It then determines the protein identity information associated with the core feature vector index. Based on the core feature vector corresponding to the healthy digestive tract tissue, a first protein abundance is determined. Based on the core feature vectors of the multiple diseased digestive tract tissues, multiple second protein abundances are determined. The method calculates the fold change of each of the multiple second protein abundances relative to the first protein abundance, and the significant difference value of each of the multiple second protein abundances. From the multiple second protein abundances, the method determines the target protein abundance corresponding to the fold change and significant difference value that meet preset conditions. Finally, based on the protein identity information corresponding to the target protein abundance, the method outputs a protein differential quantification list. This method achieves fully automated parsing from ex vivo tissue samples to a digital protein differential quantification list. By introducing healthy digestive tract tissue as a benchmark, a two-dimensional feature extraction based on retention time and mass-to-charge ratio information is performed on the mass spectra, constructing a core feature vector with multi-dimensional physicochemical properties, thus achieving accurate digital mapping of protein abundance information. Combined with automatic protein identity association based on false positive rate filtering, a high degree of consistency between identity retrieval and abundance quantification at the indexing level is ensured. Subsequently, by performing joint statistical screening on the fold change and significance value of differences between diseased and healthy digestive tract tissues, random fluctuation background is automatically removed under multi-sample dimensions, accurately locking the target protein abundance that meets preset conditions. This fully automated mass spectrometry processing not only avoids the efficiency bottleneck of manual analysis of massive amounts of spectra, but also solves the shortcomings of existing technologies, such as severe interference from endogenous enzymes in digestive tract tissues, high background heterogeneity, and low accuracy of protein differential quantification lists. It achieves a true mapping of protein components in isolated tissues and high-confidence capture of weak expression differences, thereby outputting a highly accurate protein differential quantification list. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of the mass spectrometry processing method based on digestive tract tissue provided in the embodiments of this application; Figure 2 This is a schematic diagram of the base peak chromatogram of a digestive tract tissue sample provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the mass spectrometry processing system based on digestive tract tissue provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that the execution entity involved in the embodiments of this application can be a mass spectrometry processing system based on digestive tract tissue, or it can be an electronic device. Optionally, the electronic device may include: a computer / laptop, a mobile terminal, a server, a cloud processor, an embedded image processing module, and an automated detection terminal, etc.

[0022] The following uses an electronic device as an example to illustrate in detail the mass spectrometry processing method based on digestive tract tissue provided in the embodiments of this application: Figure 1 This is a schematic flowchart of the mass spectrometry processing method based on digestive tract tissue provided in the embodiments of this application. Figure 1 As shown, the method includes the following steps 101-105.

[0023] Step 101: Obtain the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues.

[0024] Among them, multiple ex vivo digestive tract tissues refer to ex vivo specimens of tissues such as esophagus, stomach, small intestine, or large intestine obtained from a living organism.

[0025] A mass spectrum is a digital spectrum generated by liquid chromatography-mass spectrometry (LC-MS) to detect isotopically labeled and fractionated peptides from digestive tract tissue, recording the relationship between mass-to-charge ratio and ion signal intensity. The underlying data carrier of this mass spectrum is a raw mass spectrometry data file (.raw), which fully encapsulates the physical response signals of all primary precursor ions and secondary fragment ions captured within the chromatographic elution time window.

[0026] For example, such as Figure 2 As shown, the mass spectrum is visualized as a base peak chromatogram (abbreviated as: base peak chromatogram). The base peak chromatogram is a trajectory diagram obtained by extracting the maximum ion intensity signal at each scanning time point in the original mass spectrometry data, with retention time (RT) as the horizontal axis and relative abundance (Relative Abundance) as the vertical axis. It is used to intuitively characterize the dynamic elution distribution of peptides in digestive tract tissues during the chromatographic separation process.

[0027] It should be noted that the aforementioned multiple digestive tract tissues constituted the control group (healthy digestive tract tissues) and the experimental group (pathological digestive tract tissues). By processing the healthy group and multiple pathological groups in parallel, the biological differences between samples were converted into mass shift signals in the mass spectrometry using tandem mass tags (TMT), thereby eliminating batch errors and accurately anchoring key differentially expressed proteins under the same detection system.

[0028] In step 101, the electronic device acquires raw mass spectrometry data corresponding to multiple digestive tract tissues and uses a data analysis algorithm to extract the maximum ion signal within the retention time period from the aforementioned .raw file to construct the base peak diagram for each sample. During this process, the biological expression differences between the healthy group and multiple diseased groups are converted into specific mass difference signals in the mass spectrometry diagram. This step, by parallelizing the processing and data integration of healthy and diseased digestive tract tissues, enables signal normalization under the same detection system, effectively eliminating batch errors caused by instrument fluctuations or operational differences.

[0029] In some embodiments, the electronic device acquires mass spectra corresponding to multiple isolated digestive tract tissues, which may include: preprocessing and quantifying proteins in each isolated digestive tract tissue to obtain quantified proteins; performing FASP enzymatic digestion on the quantified proteins using a filter membrane to obtain multiple first peptides; specifically labeling the multiple first peptides with isotope tags to obtain multiple second peptides, each carrying a mass difference signal to be detected; mixing the multiple second peptides corresponding to each of the multiple digestive tract tissues to obtain mixed peptides; performing liquid chromatography fractionation on the mixed peptides using high-pH reversed-phase chromatography to obtain multiple component third peptides; performing liquid chromatography-mass spectrometry analysis on the third peptides and capturing the reporter ion intensity corresponding to each isotope tag in the third peptides in real time; and resolving the reporter ion intensity to obtain mass spectra corresponding to each of the multiple digestive tract tissues.

[0030] In this context, quantified protein refers to protein samples that have undergone physical disruption and chemical lysis extraction, and after concentration determination, are aliquoted according to preset quality standards (e.g., 20 μg per sample). This step aims to ensure that each sample has a uniform starting biomass in subsequent enzymatic digestion and labeling processes.

[0031] FASP enzymatic hydrolysis is a protein sample pretreatment technique based on ultrafiltration tubes. This technique uses a filter membrane to intercept large protein molecules and denature, reduce, alkylate, and degrade them with trypsin on the membrane. It can effectively remove common mucins, lipids, and salts that interfere with digestive tract tissues, producing high-purity polypeptide fragments (i.e., multiple first peptide fragments).

[0032] An isotope tag refers to a chemical reagent with the same chemical structure but containing different amounts of stable isotopes. It can specifically modify the amino terminus or lysine residues of a peptide, allowing samples from different sources to be distinguished by reporter ion signals within a specific mass-to-charge ratio window in mass spectrometry after mixing. This results in multiple second peptides carrying signals indicating the mass difference to be detected. Optionally, this isotope tag is a TMT tag.

[0033] High-pH reversed-phase chromatography is a technique for separating mixed peptides under alkaline conditions (typically pH > 10). The peptides fractionated and collected by this method are the third peptide. This method can form orthogonal separation with high-performance liquid chromatography before subsequent mass spectrometry detection, greatly reducing the complexity of peptide components and thus mitigating the masking effect of high-abundance proteins on low-abundance characteristic proteins.

[0034] Liquid chromatography-mass spectrometry (LC-MS) analysis refers to the online connection of high-performance liquid chromatography (HPLC) at nanoliter flow rates with high-resolution mass spectrometry (MS). HPLC is responsible for separating complex peptide components (i.e., the third peptide fragment) in chronological order. The separated peptide fragments are then converted into ionized peptides by an ionization source. High-resolution mass spectrometry (HS-MS) is responsible for capturing the mass information (primary mass spectrometry information) and fragmentation information (secondary mass spectrometry information) of the ionized peptides in real time.

[0035] Reported ion intensity refers to the intensity of fragment ion signals released by isotopic tags within a specific mass-to-charge ratio window when secondary fragmentation information of ionized peptides is captured by high-resolution mass spectrometry. This intensity value is positively correlated with the relative abundance of corresponding proteins from different tissue sources in the mixed sample and is a core data point for achieving automated differential quantification analysis.

[0036] Example 1, taking an isolated gastric mucosal tissue sample as an example, after obtaining the quantified protein sample, the electronic device performs the following operations to generate raw mass spectrometry data: S1. This electronic device performs protein quality assessment. Specifically, it controls the pipetting module to extract 20µg of protein from each sample and automatically adds 6X loading buffer. It then drives the heating module to perform a boiling water bath for 5 minutes to denature the protein. Subsequently, the device controls the pipetting module to add the denatured sample to the electrophoresis tank, sets a constant voltage of 250V and a duration of 40 minutes for 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Coomassie brilliant blue staining imaging technology is used to verify the integrity and consistency of protein bands in each group of samples, ensuring the representativeness of the samples in subsequent experiments.

[0037] S2. This electronic device performs FASP digestion on each sample. Specifically, it first adds dithiothreitol (DTT) to a final concentration of 100 mM and drives the heating module to perform a boiling water bath for 5 min to open the disulfide bonds in the protein. After cooling to room temperature, the device drives the mixing module to add 200 μL of urea buffer (Urea / Tris-HCl Buffer, UA) and transfers it to a 30 kD ultrafiltration centrifuge tube. Centrifugation at 12500 g for 25 min is performed twice to remove surfactants and impurities. Then, 100 μL of iodoacetamide (IAA) buffer is added and the mixture is reacted at 600 rpm in the dark for 30 min to perform alkylation. After multiple washes, trypsin solution is added, and the mixture is incubated at 37°C for 16-18 h. Finally, the device is replaced with a new collection tube and centrifuged at 12500 g. The collected filtrate is the first peptide fragment.

[0038] S3, the electronic device performs TMT labeling and liquid chromatography fractionation to generate peptide components carrying mass difference signals. Specifically, the electronic device precisely samples 100 μg from the first peptide of each sample, and modifies the N-terminus or lysine residue of the peptide with isotope tags according to the TMT labeling kit rules to obtain the second peptide carrying the mass difference signal. Subsequently, the electronic device mixes multiple groups of labeled second peptides in equal volumes and schedules a high-performance liquid chromatography system to perform liquid chromatography fractionation using a high-pH reversed-phase method. During the fractionation process, the electronic device dynamically maintains the mixing ratio of solution A (an alkaline aqueous solution composed of 10 mM ammonium formate and 5% acetonitrile, pH 10.0) and solution B (an organic phase solution composed of 10 mM ammonium formate and 85% acetonitrile, pH 10.0) through the pump system, so that the sample is loaded into the chromatographic column equilibrated with solution A via an autosampler, and the preset liquid chromatography gradient program is strictly executed at a constant flow rate of 1 mL / min. The procedure specifically includes: an equilibration period of 0-25 min (solvent B maintained at 0%); an initial ramp-up period of 25-30 min (solvent B linearly increases from 0% to 7%); a core separation period of 30-65 min (solvent B linearly increases from 7% to 40% to achieve high-resolution elution of most peptides); and subsequent elution (65-70 min, solution B increases to 100%) and washing (70-85 min, solution B maintained at 100%). During this separation process, the electronic device monitors the absorbance value at 214 nm in real time to accurately capture the elution signal of the peptides and simultaneously drives the fraction collector to collect one eluent sample every 1 min, obtaining a total of approximately 40 original fractions. Finally, after recording the lyophilization process of each component, the electronic device controls the pipetting module to reconstitute the lyophilized peptides using a 0.1% formic acid (FA) solution, and automatically merges the 40 components into 3 based on the orthogonality principle of retention time, ultimately obtaining a third peptide with significantly reduced component complexity, providing a standardized sample source for subsequent high-sensitivity mass spectrometry detection.

[0039] S4. After obtaining the three merged third peptide fractions, the electronic device performed online coupled analysis using a high-performance liquid chromatography (HPLC) system with a nanoliter flow rate and a high-resolution mass spectrometer. First, the electronic device drove the HPLC system to perform gradient elution using a 0.1% formic acid aqueous solution and a 0.1% formic acid-acetonitrile aqueous solution (acetonitrile concentration 80%). Specifically, the electronic device controlled the autosampler to load the third peptide fraction into the nano-analytical column equilibrated with formic acid aqueous solution, setting the flow rate to 300 nL / min, and executing a fine HPLC gradient program for a total duration of 1 hour: 0-3 min maintaining 6% solution B; 3-45 min linearly increasing the formic acid-acetonitrile aqueous solution ratio from 6% to 28% to achieve stable peptide elution; 45-50 min further increasing to 38%; and finally, 55-60 min maintaining the formic acid-acetonitrile aqueous solution at 100% to thoroughly clean the column. This acidic separation process, combined with the aforementioned high-pH reversed-phase chromatography method, formed orthogonal separation, maximizing the separation efficiency of digestive tract tissue peptides. Subsequently, the separated peptides were converted into positive ions online via an ionization source and then analyzed in real time for 60 minutes using a high-resolution mass spectrometer. During this process, the electronic device drove the high-resolution mass spectrometer to perform a first-stage full scan in the range of 350-1800 m / z, with a resolution set to 70,000, an automatic gain control target of 3e6, and a maximum injection time of 50 ms. For each precursor ion captured in the first-stage scan, the electronic device automatically acquired 10 fragment spectra according to a data-dependent strategy and used a 2 m / z isolation window to screen for target ions. In the second-stage mass spectrometry detection, the electronic device drove the high-resolution mass spectrometer in high-energy collision dissociation mode, setting the second-stage resolution to 35,000, the microscopic scans (microscans) to 1, the maximum injection time to 45 ms, and performing dissociation at a normalized collision energy of 30 eV. During this process, the electronic device captures and records peptide fragment information and TMT reporter ion intensity in real time, and finally generates raw mass spectrometry data (i.e., mass spectra) containing full qualitative and quantitative information. This realizes the initial conversion of physical detection signals into digital feature space, and provides a standardized data source for the subsequent automated analysis of the core feature vectors of the pathological state of isolated tissues such as gastric mucosa.

[0040] It should be noted that the FASP enzymatic digestion technology deeply removes mucin and matrix interference from digestive tract tissues, ensuring the purity and representativeness of peptide samples. Furthermore, the spatial orthogonal separation achieved by combining high-pH reversed-phase chromatography and acid mass spectrometry effectively weakens the masking effect of high-abundance proteins on low-abundance pathological signals. Simultaneously, the real-time and precise capture of reporter ion intensity using isotope labeling and high-resolution mass spectrometry integrates multiple samples into the same detection system. This completely eliminates batch errors while achieving precise digital characterization of protein expression fluctuations, thus laying a high signal-to-noise ratio data foundation for outputting highly accurate protein differential quantification lists.

[0041] In some embodiments, the electronic device pre-treats and quantifies proteins in isolated digestive tract tissue to obtain quantified proteins. This process may include: adding SDT lysis buffer to isolated digestive tract tissue and homogenizing it using a homogenizer to obtain tissue lysis buffer; subjecting the tissue lysis buffer to a boiling water bath to inactivate endogenous proteases in the tissue lysis buffer to obtain denatured lysis buffer; centrifuging the denatured lysis buffer and collecting the supernatant; and quantifying the proteins in the supernatant using the dioctanine BCA method to obtain quantified proteins.

[0042] The SDT lysis buffer is a complex system composed of a strong denaturant, a reducing agent, and a buffer solution. Through the synergistic effect of chemical dissociation and reduction, it achieves efficient extraction of complex proteins from digestive tract tissues. Optionally, the strong denaturant can be sodium dodecyl sulfate (SDS), the reducing agent can be dithiothreitol, and the buffer solution can be tris(hydroxymethyl)aminomethane hydrochloride (Tris-HCl).

[0043] Homogenization equipment refers to an automated device that can provide high-frequency oscillation or shear force to transform solid tissue into a liquid suspension in a very short time. Optionally, homogenization equipment can be a homogenizer, a tissue grinder, or an ultrasonic homogenizer.

[0044] Boiling water bath treatment refers to using a constant temperature environment of 100°C to induce irreversible denaturation of proteins, thereby completely interrupting the enzyme kinetic pathway and preventing protein degradation during the extraction process.

[0045] Centrifugation refers to the process of using centrifugal force generated by high-speed rotation to separate solids and liquids, and to precisely extract the liquid phase containing the target protein (i.e., the supernatant), while filtering out cell debris and insoluble impurities.

[0046] The Bicinchoninic Acid Assay (BCA) is a method based on the reaction of proteins with Cu under alkaline conditions. 2+ Reduced to Cu+, and Cu + A colorimetric quantitative technique that can specifically bind with dioctanine to form a purple chelate has high sensitivity and stability, and is well compatible with the high concentration of detergent components contained in SDT lysis buffer, ensuring the accuracy of protein concentration determination in digestive tract tissues.

[0047] Example 2, in conjunction with Example 1, the electronic device first obtains isolated gastric mucosal tissue and places it in a preset matrix disruption tube A (Lysing Matrix A), then injects an appropriate amount of SDT lysis buffer, and the electronic device starts an MP homogenizer (e.g., FastPrep-245G homogenization system), setting the process parameters as follows: speed 6.0 M / S, duration 60 s, and 2 cycles. The tissue is disrupted through high-energy mechanical shearing to obtain a preliminarily disrupted tissue lysis buffer.

[0048] Next, the electronic device boils the tissue lysate in a water bath for 10 minutes to irreversibly denature and inactivate endogenous proteases in the tissue, resulting in a chemically stable denatured lysate. Subsequently, a centrifuge is driven to centrifuge the denatured lysate at a centrifugal force of 14,000 g for 15 minutes, using the sedimentation principle to precipitate insoluble substances such as cell debris. Then, the electronic device controls the pipetting module to extract the supernatant and drives the supernatant through a porous membrane centrifuge tube with a pore size of 0.22 micrometers for precision filtration. The collected filtrate is the extract containing the protein to be tested.

[0049] Finally, the electronic device invokes its built-in BCA quantification algorithm to automatically map a standard curve and calculate the protein concentration of the extract based on the absorbance value collected by the microplate reader at a wavelength of 562 nm. According to the calculated protein concentration and the preset dispensing volume, the electronic device controls the pipetting module to automatically dispense the extract, obtaining quantified protein (i.e., a protein sample with known concentration and controllable quality), and records the storage environment as -20°C.

[0050] Understandably, by calculating the precise concentration of each sample, the electronic device can sample at the same mass (e.g., 20 μg) in subsequent experiments, thereby ensuring the consistency of the initial total amount of each sample during parallel analysis.

[0051] It should be noted that by using electronic devices to coordinate the efficient SDT lysis, high-energy homogenization, and boiling water bath, the original pathological characteristics of proteins are locked in while ensuring their full release, thus guaranteeing the integrity and stability of the samples. Combined with centrifugation and microfiltration technology, matrix interference is thoroughly removed, significantly reducing chemical background noise. Furthermore, relying on the BCA quantification algorithm, the initial total amount is uniformized across samples, fundamentally eliminating systematic errors and constructing a pure and standardized data foundation for obtaining a highly accurate protein differential quantification list.

[0052] Step 102: For each mass spectrum, automatically extract the retention time and mass-to-charge ratio information corresponding to each major peak in the mass spectrum to obtain the core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; and determine the protein identity information associated with the core feature vector index.

[0053] Major peaks refer to ion peaks in the mass spectrum whose signal response intensity exceeds a preset noise threshold. These major peaks represent peptide signals with high signal-to-noise ratios in the sample and are effective candidates for quantitative analysis.

[0054] Retention time refers to the time it takes for a peptide to be injected into the liquid chromatography column and then eluted and captured by a mass spectrometer detector as it passes through the liquid chromatography column with the mobile phase. It reflects the hydrophobic physicochemical properties of the peptide.

[0055] Mass-to-charge ratio (m / z) refers to the ratio of the mass (m) of a peptide ion to its charge (z), and is a fundamental physical property for mass spectrometers to separate and identify ions using a spatial electric field.

[0056] The core feature vector refers to the set of digital features obtained by spatial clustering with RT as the horizontal axis, m / z as the vertical axis, and signal intensity as the Z-axis. It is used to characterize the expression abundance of proteins in the corresponding digestive tract tissue.

[0057] Protein identity information refers to the unique identifier of a protein determined through peptide sequence alignment. Optionally, this protein identity information includes, but is not limited to: protein name, database accession number, gene symbol, and information about the material source node to which it belongs.

[0058] In step 102, the electronic device automatically extracts the retention time and mass-to-charge ratio information of each major peak in the mass spectrum corresponding to the healthy digestive tract tissue, obtaining a core feature vector characterizing the protein abundance of the corresponding healthy digestive tract tissue. This core feature vector corresponds to the healthy digestive tract tissue. Specifically, a peak detection algorithm can be used to automatically extract the retention time and mass-to-charge ratio information of each major peak in the mass spectrum. By performing spatial clustering with RT as the horizontal axis, m / z as the vertical axis, and signal intensity as the Z-axis, the core feature vector corresponding to the healthy digestive tract tissue is obtained. Then, the electronic device determines the protein identity information associated with the core feature vector index corresponding to the healthy digestive tract tissue based on the secondary fragment ion mass-to-charge ratio information in the core feature vector.

[0059] Similarly, for the mass spectra of various diseased digestive tract tissues, the electronic device can automatically extract the retention time and mass-to-charge ratio information of each major peak in the mass spectrum, and obtain the core feature vector characterizing the protein abundance of the corresponding diseased digestive tract tissue. This core feature vector corresponds to the diseased digestive tract tissue, and then, based on the mass-to-charge ratio information of secondary fragment ions in the core feature vector corresponding to the diseased digestive tract tissue, the protein identity information associated with the core feature vector index corresponding to the diseased digestive tract tissue can be determined.

[0060] This step identifies the corresponding protein based on each core feature vector, which is a prerequisite for subsequent quantitative analysis of protein differences. By establishing a one-to-one correspondence between signal features and biomolecules, qualitative identification of the proteome of the digestive tract tissue is achieved.

[0061] In some embodiments, the electronic device determines the protein identity information associated with the core feature vector index, which may include: the electronic device acquiring a protein sequence database corresponding to the digestive tract tissue corresponding to the mass spectrum, and simulating the FASP enzymatic digestion rules and isotopic label modification rules for filter-assisted sample preparation to generate a theoretical peptide library with theoretical quality information; the electronic device performs fingerprint comparison between the secondary fragment ion mass-to-charge ratio information in the core feature vector and the theoretical fragment ions in the theoretical peptide library, and calculates the association score for each matching entry; the electronic device filters the association scores based on preset primary and secondary quality tolerances to lock in candidate peptide sequences corresponding to the core feature vector; if the false positive rate of the candidate peptide sequence is less than a preset threshold, the electronic device maps the candidate peptide sequence to the corresponding protein to determine the protein identity information associated with the core feature vector index.

[0062] Protein sequence databases refer to structured datasets containing amino acid sequence information for all proteins from known organisms, providing theoretical templates for mass spectrometry identification. Optionally, the protein sequence database is a Universal Protein Resource (UniProt) database.

[0063] The isotopic tag modification rule refers to a computational strategy that adds the theoretical mass increment of the isotopic tag to the N-terminus of the peptide or to a specific amino acid residue.

[0064] A theoretical peptide library with theoretical quality information refers to a set of theoretical mass-to-charge ratio values ​​that are calculated by using computer simulations of trypsin to cut protein sequences in a database, and combining the quality contribution of TMT tags and the chemical modification of amino acid residues. This set includes all possible peptides and fragment ions.

[0065] The first-level mass tolerance is the maximum deviation between the experimentally measured mass-to-charge ratio of the precursor ion and the theoretical peptide mass allowed during peptide alignment. It is typically used to limit the search range for candidate peptides.

[0066] Secondary mass tolerance refers to the maximum deviation between the experimentally measured mass-to-charge ratio of secondary fragment ions and the theoretical fragment mass during fragment ion matching. The accuracy of this value directly affects the accuracy of fingerprint matching.

[0067] The false positive rate (FDR) refers to the proportion of incorrect results in the identification results due to random matching, and is usually evaluated using a target-reverse database search strategy.

[0068] Example 3, in conjunction with Example 1, after acquiring the raw mass spectrometry data (.raw file), the electronic device performs the following process to determine the protein identity information associated with the core feature vector index: First, the electronic device acquires the UniProt protein sequence database corresponding to isolated gastric mucosal tissue. Following the experimental procedure, it simulates the FASP enzymatic digestion rules and TMT tag modification rules to generate a theoretical peptide library with theoretical mass information. Specifically, the electronic device uses a computer to simulate the cleavage sites of trypsin on protein sequences in the database and adds the theoretical mass increment of the TMT tag to the N-terminus or lysine residue of the peptide. Simultaneously, combined with chemical modifications such as cysteine ​​alkylation, it calculates a set of theoretical mass-to-charge ratio values ​​containing all possible peptide precursor ions and secondary fragment ions, thus constructing a theoretical peptide library with theoretical mass information.

[0069] Next, the electronic device uses integrated data analysis software (such as Proteome Discoverer 2.1) to preprocess the generated raw mass spectrometry data, converting the .raw file containing continuous physical signals into a generic format file (Mascot Generic Format, .mgf file) containing discrete centroid peak information. During this process, the electronic device automatically extracts the retention time and mass-to-charge ratio information corresponding to each major peak using a peak detection algorithm, forming a set of core feature vectors to be retrieved.

[0070] Subsequently, the electronic device uses built-in tools to extract the core feature vector set and compares the mass-to-charge ratio information of secondary fragment ions in each core feature vector with the theoretical fragment ions in the aforementioned theoretical peptide library using fingerprint comparison. During the comparison process, the electronic device performs real-time filtering on the association scores of each matching entry based on preset primary and secondary mass tolerances, outputs the candidate peptide sequence and association score corresponding to each core feature vector index, and encapsulates and stores the candidate peptide sequence and association score in a search result data file (Mascot Data File, .dat file).

[0071] Finally, the electronic device extracts the .dat file generated by the probability retrieval server and executes a target-reverse database search strategy, using the calculated FDR (Frequency Detection Ratio) to assess the reliability of the identification results. If the FDR of a candidate peptide sequence is less than a preset threshold (e.g., FDR < 0.01), the electronic device determines the match to be highly reliable and maps the candidate peptide sequence to the corresponding protein, ultimately determining the protein identity information associated with the core feature vector index. If the FDR of a candidate peptide sequence is greater than or equal to the preset threshold, the electronic device determines the matching result to be noise interference from random matching or an unreliable identification, automatically discarding the candidate peptide sequence and its corresponding core feature vector, and not including them in the subsequent protein differential quantification analysis process. This bidirectional threshold-based filtering mechanism ensures the generation of high-confidence biological features.

[0072] It should be noted that by simulating FASP enzymatic digestion and modification rules to construct a precise theoretical peptide library, and combining multidimensional quality tolerance screening and false positive rate control, a high-confidence capture of weak protein expression differences was achieved at the algorithmic level. This not only ensures the accuracy of the mapping between core feature vectors and protein identity information, but also significantly reduces the false positive rate of the differential quantification list. It provides high signal-to-noise ratio and highly scientific data support for revealing the true protein composition status at the moment of gastrointestinal tissue pathology, overcoming the signal interference problem caused by the highly heterogeneous matrix and endogenous enzymes in gastrointestinal tissues.

[0073] Step 103: Determine the first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue; and determine multiple second protein abundances based on the core feature vectors of multiple diseased digestive tract tissues.

[0074] The first protein abundance refers to the quantitative value that characterizes the expression level of a specific protein in healthy digestive tract tissue after normalization.

[0075] The second protein abundance refers to the quantitative value, after normalization, that characterizes the expression level of a specific protein in various diseased digestive tract tissues.

[0076] In step 103, the electronic device takes the core feature vector set output in step 102 as input, and retrieves the core feature vectors corresponding to the healthy control group and multiple diseased experimental groups in parallel. It then analyzes the isotope reporter ion intensity integral values ​​associated with specific protein identity information within these core feature vectors and normalizes them to a preset reference channel or signal benchmark. This automatically calculates and batches the first protein abundance representing the healthy baseline and multiple second protein abundances representing protein expression levels under specific pathological conditions. This step achieves a digital conversion from abstract feature vectors to specific quantitative values, providing structured abundance data input for subsequent analysis of inter-group differences.

[0077] In some embodiments, the electronic device determines the first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue, which may include: the electronic device extracting the reporter ion intensity within a preset mass-to-charge ratio range from the core feature vector corresponding to the healthy digestive tract tissue; the electronic device summing up the reporter ion intensities of all peptides belonging to the same protein based on the retention time and peptide sequence information in the reporter ion intensity to obtain the original abundance value of the protein dimension; the electronic device performing median normalization on the original abundance value to align the signal benchmarks between different isotope channels to obtain the first protein abundance.

[0078] The reporter ion intensity refers to the ion response intensity generated by the isotope tag in secondary mass spectrometry detection within a specific low mass-to-charge ratio region (e.g., 126-131 m / z). The retention time and peptide sequence information of the reporter ion intensity refer to the physical time index at which the precursor ion of the peptide to which the reporter ion belongs is eluted in the liquid chromatography column and detected by the mass spectrometer, and the amino acid sequence identifier determined by secondary fragment ion fingerprinting, respectively.

[0079] The raw abundance value of a protein refers to the initial, uncalibrated relative abundance value of a protein obtained by summing the response signals of all characteristic peptides belonging to the same protein.

[0080] Median normalization refers to calculating the calibration factor for each sample and linearly scaling the original abundance based on the median of the original abundance of all proteins in the sample, in order to eliminate inter-channel signal drift caused by non-biological factors.

[0081] Example 4, in conjunction with Example 3, after identifying the protein, the electronic device performs protein quantification and statistical evaluation to obtain a standardized abundance matrix. First, the electronic device retrieves the set of core feature vectors generated in step 102 and uses a digital filter to accurately extract the reported ion intensity located within a preset mass-to-charge ratio window (such as the TMT 6plex tag feature window) from each core feature vector.

[0082] Secondly, this electronic device utilizes the consistency of retention time and the unique mapping relationship of peptide sequence information to cluster discrete peptide hierarchical signals in three-dimensional space. Specifically, the electronic device performs a summation calculation on all valid peptide signals mapped to the same protein identity. Through the superposition effect of peptide signals, the electronic device can offset the errors caused by fluctuations in ionization efficiency of individual peptides, thereby outputting the original abundance value of the protein dimension corresponding to the sample.

[0083] Finally, the electronic device drives its built-in normalization module, taking the raw abundance values ​​of the protein dimension corresponding to the sample as input, and executes a median normalization algorithm. Specifically, it automatically calculates the global median of the raw abundance of all proteins in the current sample and uses this as a calibration benchmark to perform linear scaling on the abundance of each protein. By adjusting the signal distribution of each sample to a uniform benchmark level, the electronic device eliminates non-biological differences introduced by fluctuations in preprocessing and detection, and finally outputs the first protein abundance corresponding to healthy digestive tract tissue.

[0084] It should be noted that the process by which the electronic device determines the abundance of multiple second proteins based on the core feature vectors of multiple diseased digestive tract tissues is similar to the process described above, and will not be repeated here. Through in-depth analysis and summarization of the underlying feature vectors, a precise digital characterization of the proteome expression level of digestive tract tissues is achieved. By utilizing the median normalization mechanism, systematic biases caused by preprocessing processes (such as slight differences in FASP enzymatic digestion efficiency) or instrument fluctuations are eliminated at the algorithmic level, laying a standardized data foundation for subsequent screening of biologically significant differential biomarkers and the establishment of precise auxiliary diagnostic models for digestive tract diseases.

[0085] Step 104: Calculate the fold difference of each second protein abundance relative to the first protein abundance, and the significance difference value of each second protein abundance.

[0086] Among them, the fold change (FC) refers to the ratio of the change in protein expression level in diseased digestive tract tissues to that in healthy digestive tract tissues, and is used to characterize the degree of upregulation or downregulation of proteins during the disease process.

[0087] The probability value (P-value) is a probabilistic indicator used to measure whether observed abundance differences are statistically significant, and is used to distinguish between real biological differences and random experimental errors.

[0088] In step 104, the electronic device takes the structured abundance data (i.e., the first protein abundance and multiple second protein abundances) output in step 103 as input, and transforms the discrete abundance values ​​into differential feature vectors (i.e., fold change and significance value) with pathological analysis value by performing fold change calculation and distribution feature evaluation in parallel. This step, by introducing distribution evaluation, transforms discrete abundance values ​​into differential features with pathological analysis value, which is the core of achieving high-confidence capture of weak expression differences.

[0089] In some embodiments, the electronic device calculates the difference folds of multiple second protein abundances relative to a first protein abundance, which may include: performing the following operations for each second protein abundance: the electronic device uses the ratio of the second protein abundance to the first protein abundance as the original difference fold; the electronic device performs a logarithmic transformation on the original difference fold to a base-2 value to obtain a logarithmic difference value; the electronic device uses the logarithmic difference value as the difference fold of the second protein abundance relative to the first protein abundance.

[0090] In this embodiment, the electronic device retrieves the structured abundance data output in step 103 as algorithm input, which includes the first protein abundance of the healthy baseline and multiple second protein abundances of each lesion sample. The electronic device calculates the ratio of each second protein abundance to the first protein abundance to obtain the original difference fold, and performs a base-2 logarithmic transformation on it (i.e., log2 transformation) to generate a difference fold that is symmetrically distributed in a linear coordinate system.

[0091] It should be noted that by using logarithmic transformation to convert the fold relationship into a linear relationship, the upregulation and downregulation of protein expression are symmetrically distributed on the numerical axis (e.g., 2-fold upregulation corresponds to +1, and 0.5-fold downregulation corresponds to -1). The entire process effectively compresses the interference of high-abundance outliers and amplifies the weak fold changes commonly found in digestive tract tissues, providing a standardized data format for setting precise screening thresholds under unified dimensions.

[0092] In some embodiments, the electronic device calculates the significant difference values ​​of each of the multiple second protein abundances, which may include: the electronic device acquiring a first replicate sample corresponding to healthy digestive tract tissue and acquiring first abundance distribution data corresponding to the first replicate sample; for each second protein abundance, the electronic device acquiring a second replicate sample corresponding to diseased digestive tract tissue corresponding to the second protein abundance and acquiring second abundance distribution data corresponding to the second replicate sample; the electronic device using the statistical difference between the second abundance distribution data and the first abundance distribution data as the significant difference value of the second protein abundance.

[0093] The first / second replicate sample refers to a biological or technical replicate sample set up to eliminate experimental randomness.

[0094] Statistical difference refers to the degree of overlap in probability density between two sets of distributed data that does not involve random fluctuations.

[0095] In this embodiment, firstly, the electronic device retrieves a first replicate sample corresponding to healthy digestive tract tissue and automatically aggregates and generates a first abundance distribution dataset based on the core feature vector index locked in step 102. This first abundance distribution dataset, through digital representation, defines the endogenous abundance fluctuation range of a specific protein under healthy physiological conditions. Next, for each second protein abundance, the electronic device automatically locates the corresponding pathological digestive tract tissue and retrieves the corresponding second replicate sample. For this second replicate sample, the electronic device generates a second abundance distribution dataset based on the core feature vector of the second replicate sample through parallel computing logic, to characterize the distribution characteristics of the protein under specific pathological conditions. Finally, the electronic device drives the built-in statistical testing engine to perform a full comparison between the second abundance distribution dataset and the first abundance distribution dataset. The electronic device automatically calculates the probability that the two sets of distribution data do not belong to random fluctuations by executing preset statistical testing algorithms (such as T-test or ANOVA), and quantifies this probability as a significant difference value for output.

[0096] It should be noted that by introducing distribution data from repeated samples, the mean shift and standard deviation between each group are automatically calculated. The entire process eliminates individual random noise caused by the complex physicochemical properties of the digestive tract tissue, and combines statistical significance with fold change to ensure that the quantitative results of the selected differences are biologically reproducible, fundamentally solving the technical defect of high false positive rate in traditional proportional analysis methods.

[0097] Step 105: From multiple second protein abundances, determine the target protein abundance corresponding to the difference fold and significance value that meet the preset conditions; and output a protein difference quantification list based on the protein identity information corresponding to the target protein abundance.

[0098] Among them, the abundance of target protein refers to the quantitative value of proteins that are determined to have significant expression differences and have pathological analysis value.

[0099] A protein differential quantification list is a structured data report that integrates the target protein’s identity information (such as gene name), quantitative characteristics (such as fold change, significance level), and expression level (such as upregulation or downregulation) to guide subsequent biological function analysis or clinical auxiliary diagnosis.

[0100] In step 105, the electronic device first performs parallel threshold screening, simultaneously iterating through the fold change and significance level of each second protein abundance, and automatically comparing each indicator with preset screening conditions. For second protein abundances that simultaneously meet both of the above conditions, the electronic device marks the second protein abundance as the target protein abundance, and performs identity information association mapping based on the mass spectrometry features from which the second protein abundance originates, accurately obtaining the corresponding protein identity information, such as protein name, gene symbol, and description, from the associated protein identification database. Finally, the electronic device performs structured list generation and output, automatically formatting and integrating the target protein abundance, fold change, significance level, and protein identity information. The electronic device generates a structured protein differential quantification list containing specific up- or down-regulation indicators according to a preset report template, and completes the final output through a specified data interface.

[0101] In this step, the electronic device acts as an intelligent filter and data integrator, accurately locating statistically and biologically significant differentially expressed proteins from massive amounts of data through automated threshold judgments, and assigning clear biological identities to the numerical results using database mapping. The entire process eliminates human intervention through digitalization, ensuring the efficiency, objectivity, and reproducibility of the final standardized report in subsequent bioinformatics analysis or clinical interpretation.

[0102] In some embodiments, the electronic device determines the target protein abundance from a plurality of second protein abundances that meets preset conditions for a fold change and a significant difference value; and outputs a protein difference quantification list based on the protein identity information corresponding to the target protein abundance. This may include: the electronic device selecting, from a plurality of second protein abundances, the second protein abundance corresponding to a fold change within a preset range and a significant difference value less than a preset difference threshold as the target protein abundance; the electronic device determining the target core feature vector corresponding to the target protein abundance; and the electronic device outputting a protein difference quantification list containing a protein access number, protein name, fold change, and significant difference value based on the protein identity information associated with the target core feature vector index.

[0103] In this embodiment, the electronic device analyzes multiple second protein abundances separately. For the current second protein abundance, if the fold change is not within a preset range and / or the significant difference value is greater than or equal to a preset difference threshold, it indicates that the expression fluctuation of the protein corresponding to the second protein abundance is small, failing to reach the preset biological regulatory threshold, or that the distribution of its abundance difference among repeated samples is too discrete, making it impossible to exclude interference from experimental noise or non-specific factors, and thus lacking confidence as a differentially expressed protein. In this case, the second protein abundance can be removed. If the fold change is within the preset range and the significant difference value is less than the preset difference threshold, it indicates that the second protein abundance not only exhibits a significant deviation in expression level sufficient to characterize a pathological state, but this deviation is not caused by experimental error or individual random fluctuations, and has high biological statistical significance. In this case, the second protein abundance can be retained and identified as the target protein abundance. Based on this, the electronic device can acquire at least one target protein abundance. Subsequently, the electronic device determines the target core feature vector corresponding to the abundance of the target protein, and extracts key identifiers such as the corresponding protein access number, protein name, and functional description based on the protein identity information associated with the index of the target core feature vector. Finally, the electronic device automatically generates and outputs a list of protein differential quantifications. This list is presented in a structured format and includes the protein access number, protein name, fold change, and significance level.

[0104] It should be noted that the preset range and preset difference threshold are decision boundaries set based on the statistical distribution characteristics of digestive tract tissue proteomics. Typically, the preset range is set to an absolute value of the logarithmic difference greater than or equal to 0.263 (i.e., the original difference fold is greater than 1.2 times or less than 0.83 times), and the preset difference threshold is set to a significant difference value P < 0.05.

[0105] Furthermore, this dual-threshold filtering mechanism can capture biologically significant expression shifts through fold change thresholds, especially for subtle protein abundance fluctuations in the early stages of gastrointestinal lesions; simultaneously, it eliminates random noise and individualized interference during the experimental process through significance thresholds. By using the target core feature vector as a bridge, it achieves precise backtracking from "numerical difference" to "biological identity," and the output protein differential quantification list not only provides a unique protein identifier (i.e., protein access number) and functional description (i.e., protein name), but also simultaneously links statistically validated quantitative features (i.e., fold change and significance value). The entire process achieves automated distillation of massive mass spectrometry data, directly solving the problems of severe heterogeneity interference and high false positive rates in gastrointestinal tissues, thus producing highly accurate and reliable protein differential quantification results.

[0106] In this embodiment, the technical solution described in steps 101-105 above achieves fully automated analysis of the entire process from isolated tissue samples to a digital protein differential quantification list. By introducing healthy digestive tract tissue as a benchmark, dual-dimensional feature extraction based on retention time and mass-to-charge ratio information is performed on the mass spectrometer, constructing a core feature vector with multi-dimensional physicochemical properties, thus achieving accurate digital mapping of protein abundance information. Combined with automatic protein identity association based on false positive rate filtering, a high degree of consistency between identity retrieval and abundance quantification at the index level is ensured. Subsequently, by performing joint statistical screening on the fold change and significance value of the difference between diseased and healthy digestive tract tissues, random fluctuation background is automatically removed under multi-sample dimensions, accurately locking the target protein abundance that meets the preset conditions. The fully automated mass spectrometry processing not only avoids the efficiency bottleneck of manual analysis of massive amounts of spectra, but also solves the shortcomings of existing technologies such as severe interference from endogenous enzymes in digestive tract tissues, high background heterogeneous impurities, and low accuracy of protein differential quantification lists. It achieves true mapping of protein components in isolated tissues and high-confidence capture of weak expression differences, thereby outputting highly accurate protein differential quantification lists.

[0107] The following describes the mass spectrometry processing system based on digestive tract tissue provided in the embodiments of this application. The mass spectrometry processing system based on digestive tract tissue described below can be referred to in correspondence with the mass spectrometry processing method based on digestive tract tissue described above.

[0108] Figure 3 This is a schematic diagram of the structure of the mass spectrometry processing system based on digestive tract tissue provided in an embodiment of this application. Figure 3 As shown, the system includes: an acquisition module 301 and a processing module 302.

[0109] The acquisition module 301 is used to acquire the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues.

[0110] Processing module 302 is used to automatically extract the retention time and mass-to-charge ratio information corresponding to each major peak in each mass spectrum to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; determine the protein identity information associated with the core feature vector index; determine a first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue; determine multiple second protein abundances based on the core feature vectors of the multiple diseased digestive tract tissues; calculate the fold change of each of the multiple second protein abundances relative to the first protein abundance, and the significant difference value of each of the multiple second protein abundances; determine the target protein abundance corresponding to the fold change and significant difference value that meet preset conditions from the multiple second protein abundances; and output a protein differential quantification list based on the protein identity information corresponding to the target protein abundance.

[0111] Optionally, module 301 is specifically used to preprocess and quantify proteins in isolated digestive tract tissues to obtain quantified proteins; to perform FASP enzymatic digestion of the quantified proteins using a filter-assisted sample preparation method to obtain multiple first peptides; to specifically label the multiple first peptides using isotope tags to obtain multiple second peptides, which carry mass difference signals to be detected; to mix the multiple second peptides corresponding to the multiple digestive tract tissues to obtain mixed peptides; to perform liquid phase fractionation of the mixed peptides using high-pH reversed-phase chromatography to obtain multiple component third peptides; to perform liquid chromatography-mass spectrometry analysis on the third peptides and capture the reporter ion intensity corresponding to each isotope tag in the third peptide in real time; and to analyze the reporter ion intensity to obtain the mass spectrum corresponding to each of the multiple digestive tract tissues.

[0112] Optionally, the acquisition module 301 is further configured to add SDT lysis buffer to the isolated digestive tract tissue and perform homogenization to obtain tissue lysis buffer; subject the tissue lysis buffer to boiling water bath treatment to inactivate the endogenous protease in the tissue lysis buffer to obtain denatured lysis buffer; centrifuge the denatured lysis buffer and collect the supernatant; and quantify the protein in the supernatant using the dioctanine BCA method to obtain the quantified protein.

[0113] Optionally, the processing module 302 is specifically used to acquire the protein sequence database corresponding to the digestive tract tissue of the mass spectrum, and simulate the FASP enzymatic digestion rules and isotopic label modification rules for filter-assisted sample preparation to generate a theoretical peptide library with theoretical quality information; compare the mass-to-charge ratio information of secondary fragment ions in the core feature vector with the theoretical fragment ions in the theoretical peptide library to calculate the association score of each matching entry; filter the association score based on the preset primary and secondary quality tolerances to lock the candidate peptide sequences corresponding to the core feature vector; if the false positive rate of the candidate peptide sequence is less than a preset threshold, map the candidate peptide sequence to the corresponding protein to determine the protein identity information associated with the core feature vector index.

[0114] Optionally, the processing module 302 is further configured to extract the reporter ion intensity within a preset mass-to-charge ratio range from the core feature vector corresponding to the healthy digestive tract tissue; sum and aggregate the reporter ion intensities of all peptides belonging to the same protein according to the retention time and peptide sequence information in the reporter ion intensity to obtain the original abundance value of the protein dimension; and perform median normalization on the original abundance value to align the signal benchmarks between different isotope channels to obtain the first protein abundance.

[0115] Optionally, the processing module 302 is further configured to perform the following operations for each second protein abundance: using the ratio of the second protein abundance to the first protein abundance as the original difference factor; performing a logarithmic transformation on the original difference factor to obtain a logarithmic difference value; and using the logarithmic difference value as the difference factor of the second protein abundance relative to the first protein abundance.

[0116] Optionally, the processing module 302 is further configured to obtain a first replicate sample corresponding to the healthy digestive tract tissue and obtain first abundance distribution data corresponding to the first replicate sample; for each second protein abundance, obtain a second replicate sample corresponding to the diseased digestive tract tissue corresponding to the second protein abundance and obtain second abundance distribution data corresponding to the second replicate sample; and use the statistical difference between the second abundance distribution data and the first abundance distribution data as the significance difference value of the second protein abundance.

[0117] Optionally, the processing module 302 is further configured to select, from the plurality of second protein abundances, the second protein abundance corresponding to the significant difference value that is within a preset range and less than a preset difference threshold as the target protein abundance; determine the target core feature vector corresponding to the target protein abundance; and output a protein difference quantification list containing protein access number, protein name, difference fold and significant difference value according to the protein identity information associated with the index of the target core feature vector.

[0118] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a mass spectrometry processing method based on digestive tract tissue.

[0119] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the mass spectrometry processing method based on digestive tract tissue provided by the above methods.

[0121] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the mass spectrometry processing methods based on digestive tract tissues provided by the methods described above.

[0122] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for mass spectrometry processing based on digestive tract tissue, characterized in that, include: Obtain the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues; For each mass spectrum, the retention time and mass-to-charge ratio information corresponding to each major peak in the mass spectrum are automatically extracted to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; and the protein identity information associated with the core feature vector index is determined. The first protein abundance is determined based on the core feature vector corresponding to the healthy digestive tract tissue. And based on the core feature vectors of each of the multiple diseased digestive tract tissues, the abundance of multiple second proteins is determined; Calculate the fold difference of the plurality of second protein abundances relative to the first protein abundance, and the significance value of each of the plurality of second protein abundances; From the plurality of second protein abundances, determine the target protein abundance corresponding to the difference fold and significant difference value that meet the preset conditions; Based on the protein identity information corresponding to the abundance of the target protein, a list of protein differential quantifications is output.

2. The method for mass spectrometry processing based on digestive tract tissue according to claim 1, characterized in that, The acquisition of mass spectra of multiple isolated digestive tract tissues includes: For each digestive tract tissue, the isolated digestive tract tissue is pretreated and protein is quantified to obtain quantified protein; the quantified protein is then subjected to FASP enzymatic digestion with filter membrane assisted sample preparation to obtain multiple first peptides; the multiple first peptides are specifically labeled with isotope tags to obtain multiple second peptides, and the multiple second peptides carry the quality difference signal to be detected. The multiple second peptide fragments corresponding to the multiple digestive tract tissues are mixed to obtain a mixed peptide fragment; and the mixed peptide fragment is fractionated by high pH reversed-phase chromatography to obtain a third peptide fragment with multiple components. The third peptide was analyzed by liquid chromatography-mass spectrometry, and the intensity of the reporter ion corresponding to each isotope tag in the third peptide was captured in real time. The intensity of the reported ions was analyzed to obtain the mass spectra of each of the multiple digestive tract tissues.

3. The method for mass spectrometry processing based on digestive tract tissue according to claim 2, characterized in that, The pretreatment and protein quantification of the isolated digestive tract tissue to obtain quantified proteins includes: SDT lysis buffer was added to the isolated digestive tract tissue, and the tissue was broken up using a homogenizer to obtain tissue lysis buffer. The tissue lysate was subjected to a boiling water bath to inactivate the endogenous proteases in the tissue lysate, resulting in a denatured lysate. The denatured lysis solution was centrifuged and the supernatant was collected; The protein in the supernatant was quantified using the bis(octoctanic acid) BCA method to obtain the quantified protein.

4. The method for mass spectrometry processing based on digestive tract tissue according to any one of claims 1-3, characterized in that, The determination of protein identity information associated with the core feature vector index includes: Obtain the protein sequence database corresponding to the digestive tract tissues corresponding to the mass spectra, and simulate the FASP enzymatic digestion rules and isotopic tag modification rules for filter membrane-assisted sample preparation to generate a theoretical peptide library with theoretical quality information. The mass-to-charge ratio information of secondary fragment ions in the core feature vector is compared with the fingerprint of theoretical fragment ions in the theoretical peptide library, and the association score of each matching entry is calculated. The correlation scores are filtered based on the preset first-level quality tolerance and second-level quality tolerance to lock in the candidate peptide sequences corresponding to the core feature vector; If the false positive rate of the candidate peptide sequence is less than a preset threshold, the candidate peptide sequence is mapped to the corresponding protein to determine the protein identity information associated with the core feature vector index.

5. The method for mass spectrometry processing based on digestive tract tissue according to any one of claims 1-3, characterized in that, The step of determining the first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue includes: From the core feature vector corresponding to the healthy digestive tract tissue, extract the reporter ion intensity within a preset mass-to-charge ratio range; Based on the retention time and peptide sequence information in the reporter ion intensity, the reporter ion intensities of all peptides belonging to the same protein are summed to obtain the original abundance value of the protein dimension. The original abundance values ​​were normalized by median to align the signal benchmarks between different isotope channels, thus obtaining the first protein abundance.

6. The method for mass spectrometry processing based on digestive tract tissue according to any one of claims 1-3, characterized in that, The calculation of the difference fold of the plurality of second protein abundances relative to the first protein abundance includes: Perform the following operations for each second protein abundance: The ratio of the second protein abundance to the first protein abundance is used as the original difference factor; The original difference multiple is subjected to a base-2 logarithmic transformation to obtain the logarithmic difference value; The logarithmic difference value is used as the difference factor of the second protein abundance relative to the first protein abundance.

7. The method for mass spectrometry processing based on digestive tract tissue according to any one of claims 1-3, characterized in that, The calculation of the significant difference values ​​of the abundance of each of the plurality of second proteins includes: Obtain a first replicate sample corresponding to the healthy digestive tract tissue, and obtain the first abundance distribution data corresponding to the first replicate sample; For each second protein abundance, a second replicate sample corresponding to the diseased digestive tract tissue corresponding to the second protein abundance is obtained, and the second abundance distribution data corresponding to the second replicate sample is obtained; the statistical difference between the second abundance distribution data and the first abundance distribution is taken as the significance value of the second protein abundance.

8. The method for mass spectrometry processing based on digestive tract tissue according to any one of claims 1-3, characterized in that, The step involves determining the target protein abundance corresponding to the difference fold and significant difference value that meet preset conditions from the plurality of second protein abundances. Based on the protein identity information corresponding to the target protein abundance, a list of differentially expressed proteins is output, including: From the plurality of second protein abundances, the second protein abundance corresponding to the significant difference value that is within a preset range and less than a preset difference threshold is taken as the target protein abundance; Determine the target core feature vector corresponding to the abundance of the target protein; Based on the protein identity information associated with the target core feature vector index, output a list of protein differential quantifications containing protein access number, protein name, fold change, and significance value.

9. A mass spectrometry processing system based on digestive tract tissue, characterized in that, include: The acquisition module is used to acquire the mass spectra of multiple isolated digestive tract tissues, including one healthy digestive tract tissue and multiple diseased digestive tract tissues. The processing module is used to automatically extract the retention time and mass-to-charge ratio information of each major peak in each mass spectrum to obtain a core feature vector characterizing the protein abundance of the corresponding digestive tract tissue; and determine the protein identity information associated with the core feature vector index; determine the first protein abundance based on the core feature vector corresponding to the healthy digestive tract tissue; and determine multiple second protein abundances based on the core feature vectors of the multiple diseased digestive tract tissues. Calculate the fold difference of the plurality of second protein abundances relative to the first protein abundance, and the significance value of each of the plurality of second protein abundances; From the plurality of second protein abundances, determine the target protein abundance corresponding to the difference fold and significant difference value that meet the preset conditions; Based on the protein identity information corresponding to the abundance of the target protein, a list of protein differential quantifications is output.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the mass spectrometry processing method based on digestive tract tissue as described in any one of claims 1-8.

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