M protein detection method, electronic device and storage medium

Abstract

The present application relates to the technical field of sample detection, and specifically provides a detection method, electronic device and storage medium for M protein, aiming to solve the technical problem of how to achieve high-throughput, high-sensitivity and low-cost qualitative and quantitative detection of M protein. To this end, the method of the present application includes: obtaining mass spectrum data of the sample to be detected, wherein the mass spectrum data includes at least a first preset interval and a second preset interval, the first preset interval is the distribution interval of λ light chain in the mass spectrum data, and the second preset interval is the distribution interval of κ light chain in the mass spectrum data; performing peak detection on the mass spectrum data to determine whether there is a mass spectrum peak in the first preset interval and the second preset interval; obtaining the detection result of M protein based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval. Through the above-mentioned implementation, high-throughput, high-sensitivity and low-cost qualitative detection of M protein can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of sample detection, and in particular to a detection method, electronic device and storage medium of M protein. Background Art

[0002] At present, monoclonal immunoglobulin (M protein) in serum is an important biomarker that can be used to screen for multiple myeloma, chronic lymphocytic leukemia and other diseases. When detecting M protein in serum, MALDI-TOF mass spectrometry (non-antibody enrichment) is more sensitive, rapid and low-cost than serum protein electrophoresis (PEL), immunofixation electrophoresis (IFE), serum free light chain (FLC) and other methods.

[0003] The existing MALDI-TOF mass spectrometry relies on the identification and interpretation of the M protein peak. Some of them compare and overlay the mass spectrum of the target sample with the negative mass spectrum of the M protein of a healthy person, and then perform manual visual inspection. If obvious peaks appear in the single-charge and double-charged mass-to-charge ratio range of the κ and λ light chains, it is determined that the serum sample to be tested contains M protein. Some of them determine that the sample to be tested contains M protein by judging that the mass spectrum peaks with narrow base, high peak and sharp peaks within the mass-to-charge ratio range of the λ light chain and the κ light chain, or by the peak area ratio and peak shape of the κ light chain and the λ light chain.

[0004] However, the mass spectrometry peak of the M protein light chain of weakly positive or double-positive patients is usually low in intensity, and obvious mass spectrometry peaks may not be observed. The first method is prone to missed detection, and it is necessary to superimpose and compare the spectrum to be tested with the negative spectrum of the M protein, which is difficult to automate. The visual interpretation results will also be affected by personnel experience and subjective factors, and consistency cannot be guaranteed, making it difficult to apply to large-scale screening of diseases. Moreover, the mass spectrometry peak area ratio of the κ light chain and λ light chain of the M protein of weakly positive or double-positive patients may be within the normal range. The second method cannot identify M proteins with low peak intensity and a mass spectrometry peak area ratio of the κ light chain and λ light chain within the normal range. The sensitivity is low, and the use of machine learning to determine the shape of the mass spectrometry peak also requires the collection of a large number of samples for training. The process is complicated and time-consuming, and the existing methods cannot provide quantitative information on the M protein.

[0005] Accordingly, the art needs a new technical solution to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects, the present application is proposed to provide a method for detecting M protein, an electronic device and a storage medium that solve or at least partially solve the technical problem of how to achieve high-throughput, high-sensitivity and low-cost qualitative and quantitative detection of M protein.

[0007] In a first aspect, a method for detecting M protein is provided, characterized in that the method comprises:

[0008] Acquire mass spectrum data of the sample to be detected; the mass spectrum data at least includes a first preset interval and a second preset interval, the first preset interval is a distribution interval of λ light chain in the mass spectrum data, and the second preset interval is a distribution interval of κ light chain in the mass spectrum data;

[0009] Performing peak detection on the mass spectrum data to determine whether there is a mass spectrum peak in the first preset interval and the second preset interval;

[0010] The detection result of the M protein is obtained based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval.

[0011] In a technical solution of the above-mentioned method for detecting M protein, obtaining the detection result of M protein based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval includes:

[0012] If the mass spectrum peak does not exist in the first preset interval and the second preset interval, it is determined that the sample to be detected does not contain M protein;

[0013] If the mass spectrum peak exists in the first preset interval and the second preset interval, obtaining the position and intensity of each mass spectrum peak;

[0014] The detection result of the M protein is obtained based on the average intensity of the mass spectrum data and the intensity of each mass spectrum peak in the first preset interval and the second preset interval.

[0015] In a technical solution of the above-mentioned method for detecting M protein, obtaining the detection result of M protein based on the average intensity of the mass spectrum data and the intensity of each mass spectrum peak in the first preset interval and the second preset interval includes:

[0016] Obtaining a ratio coefficient between the intensity of each mass spectrum peak and the average intensity of the mass spectrum data within the first preset interval and the second preset interval;

[0017] If there is at least one of the proportionality coefficients that is greater than or equal to the proportionality coefficient threshold value, it is determined that the sample to be detected contains M protein.

[0018] In a technical solution of the above-mentioned method for detecting M protein, the method further comprises:

[0019] Determine a mass spectrum peak whose proportionality coefficient is greater than or equal to the proportionality coefficient threshold value as an M protein spectrum peak;

[0020] Removing the sub-peak of the M protein spectrum peak in the peak detection result, and obtaining the type of M protein in the sample to be detected based on the remaining M protein spectrum peak;

[0021] The types of M protein include λ-type M protein positive, κ-type M protein positive, and λ-type and κ-type M protein double positive.

[0022] In a technical solution of the above-mentioned method for detecting M protein, the method further comprises:

[0023] When the sample to be detected contains the M protein, a quantitative result of the M protein is obtained based on the area of ​​the M protein spectrum peak.

[0024] In a technical solution of the above-mentioned method for detecting M protein, the mass spectrum data further includes a third preset interval, and the third preset interval is the distribution interval of glycosylated light chains in the mass spectrum data; the method further includes:

[0025] Perform glycosylation detection on the third preset interval to obtain a glycosylation detection result.

[0026] In a technical solution of the above-mentioned method for detecting M protein, performing glycosylation detection on the third preset interval to obtain the glycosylation detection result includes:

[0027] Performing peak detection on the mass spectrum data to determine whether there is a mass spectrum peak within the third preset interval;

[0028] If so, obtaining the number of mass spectrum peaks whose signal-to-noise ratio value is greater than or equal to the signal-to-noise ratio threshold value within the third preset interval; when the number of mass spectrum peaks reaches the preset number, determining that the sample to be tested is M protein glycosylation positive, otherwise determining that the sample to be tested is M protein glycosylation negative;

[0029] If not present, the sample to be tested is determined to be negative for glycosylation of the M protein.

[0030] In a technical solution of the above-mentioned method for detecting M protein, the peak value detection of the mass spectrum data includes:

[0031] Obtain continuous wavelet transform peak detection parameters;

[0032] Continuous wavelet transform peak detection is performed on the mass spectrum data based on the continuous wavelet transform peak detection parameters.

[0033] In a second aspect, an electronic device is provided, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the M protein detection method described in any one of the technical solutions of the above-mentioned M protein detection method.

[0034] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method for detecting the M protein described in any one of the technical solutions of the above-mentioned method for detecting the M protein.

[0035] The above one or more technical solutions of this application have at least one or more of the following

[0036] Beneficial effects:

[0037] In the technical solution of the present application, firstly, the mass spectrum data of the sample to be tested is obtained, wherein the mass spectrum data includes at least a first preset interval and a second preset interval, the first preset interval is the distribution interval of the λ light chain in the mass spectrum data, and the second preset interval is the distribution interval of the κ light chain in the mass spectrum data; then the mass spectrum data is subjected to peak detection to determine whether there is a mass spectrum peak in the first preset interval and the second preset interval, and finally the detection result of the M protein is obtained based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval. Through the above implementation, the mass spectrum peak of the M protein light chain of weakly positive or double positive patients can be identified, the process is simple and fast, can be automated, the consumption of reagents and consumables is small, and the test results are not easily affected by external conditions, it is easier to achieve standard unification, and can be applied to large-scale clinical testing.

[0038] Furthermore, the sample to be tested can be subjected to quantitative detection of M protein and qualitative detection of immunoglobulin light chain glycosylation, thereby achieving high-throughput, high-sensitivity, and low-cost qualitative and quantitative detection of M protein and qualitative detection of immunoglobulin light chain glycosylation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:

[0040] Figure 1a is a schematic diagram of mass spectrum peaks of a sample negative for M protein in one embodiment;

[0041] Figure 1b is a schematic diagram of mass spectrometry peaks of a sample positive for M protein in an embodiment;

[0042] Figure 1cis a schematic diagram of a mass spectrum peak of a sample with weak positive M protein in an embodiment;

[0043] Figure 2 This is a schematic flow chart of the main steps of a method for detecting M protein according to one embodiment of the present application;

[0044] Figure 3 is a schematic diagram of mass spectrum data of a sample to be detected according to an embodiment of the present application;

[0045] Figure 4 is a schematic flow chart of the main steps of performing peak detection on mass spectrum data according to one embodiment of the present application;

[0046] Figure 5 It is a schematic flow chart of the main steps of obtaining the detection result of M protein based on the average intensity of mass spectrum data and the intensity of each mass spectrum peak in the first preset interval and the second preset interval according to an embodiment of the present application;

[0047] Figure 6 is a schematic diagram of sub-peaks of the M protein spectrum peak according to one embodiment of the present application;

[0048] Figure 7a is a schematic diagram of a positive qualitative result and a quantitative result of a lambda-type M protein according to an embodiment of the present application;

[0049] Figure 7b is a schematic diagram of the qualitative result and quantitative result of λ-type M protein positive (weak positive) according to another embodiment of the present application;

[0050] Figure 7c is a schematic diagram of positive qualitative results and quantitative results of κ-type M protein according to one embodiment of the present application;

[0051] Figure 8 is a schematic diagram of a positive qualitative result of M protein glycosylation according to one embodiment of the present application;

[0052] Fig. 9 It is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

[0053] List of reference numerals:

[0054] 901: processor; 902: storage device. DETAILED DESCRIPTION

[0055] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0056] In the description of the present application, "processor" may include hardware, software or a combination of the two. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as just A, just B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include just A, just B or A and B. The singular terms "one" and "the" may also include plural forms.

[0057] Here we first explain some terms involved in this application.

[0058] Monoclonal immunoglobulin (M protein): It is a large amount of abnormal immunoglobulin produced by the malignant proliferation of plasma cells or B lymphocytes. Its essence is an immunoglobulin (Ig) or a fragment of immunoglobulin. Among them, immunoglobulin refers to a globulin with antibody activity or a chemical structure similar to that of an antibody molecule. Immunoglobulin is a tetrapeptide chain structure composed of two identical light chains and two identical heavy chains connected by interchain disulfide bonds.

[0059] Mass spectrometry: An analytical technique that ionizes a sample into charged molecules and measures their mass-to-charge ratio (m / z).

[0060] MALDI-TOF: Matrix-assisted laser desorption ionization time-of-flight mass spectrometry is an analytical instrument used in the field of biology. In MALDI-TOF mass spectrometry, the ion source is matrix-assisted laser desorption ionization and the mass analyzer is a time-of-flight analyzer.

[0061] PBS solution: It is the most widely used buffer in biochemical research. Its main components are Na2HPO4, KH2PO4, NaCl and KCl, etc. It is generally used as a solvent to dissolve and protect reagents.

[0062] Tris(2-carboxyethyl)phosphine: It is a very effective thiol reducing agent and is widely used as a quantitative reducing agent for disulfide bonds in protein chemistry and proteomics research.

[0063] Sinapic acid: A chemical matrix liquid used for mass spectrometry analysis, which is a mixture of sinapic acid and organic solvents such as methanol or ethanol. It can provide a stable matrix environment in mass spectrometry analysis, making the analyzed compounds clearer and easier to identify.

[0064] As described in the background technology, monoclonal immunoglobulin (M protein) in serum is an important biomarker that can be used to screen for multiple myeloma, chronic lymphocytic leukemia and other diseases. When detecting M protein in serum, MALDI-TOF mass spectrometry (non-antibody enrichment) is compared with serum protein electrophoresis (PEL), immunofixation electrophoresis (IFE), serum free light chain (FLC) and other methods, which has the characteristics of high sensitivity, rapidity and low cost.

[0065] MALDI-TOF mass spectrometry relies on the identification and interpretation of M protein peaks. Through a large number of sample analysis statistics, it was found that the mass spectrum peaks of M protein-negative samples have a relatively standard Gaussian distribution, while the mass spectrum peaks of M protein-positive samples have non-Gaussian distribution peaks. Figure 1a , Figure 1b and Figure 1c , Figure 1a is a schematic diagram of the mass spectrum peak of a sample negative for M protein in an embodiment, Figure 1b is a schematic diagram of mass spectrometry peaks of a sample positive for M protein in an embodiment, Figure 1c The figure is a schematic diagram of the mass spectrum peak of a sample with weak positive M protein in an embodiment.

[0066] Further, existing MALDI-TOF mass spectrometry methods include:

[0067] Method 1: After the serum is diluted and restored, the total light chains in the serum are qualitatively detected. The mass spectrum of the target sample is compared with the negative mass spectrum of the M protein of a healthy person. If obvious peaks appear in the single-charge and double-charged mass-to-charge ratio range of the κ and λ light chains, it is judged that the serum sample to be tested contains M protein.

[0068] Method 2: After the serum is diluted and restored, the total light chains in the serum are qualitatively detected to provide mass-to-charge ratio distribution data of single-charged ions of immunoglobulin light chains (including λ light chains and κ light chains) in the test sample. If the light chain mass-to-charge ratio range has a narrow base, high peak and sharp mass spectrum peak, it is determined that the test sample contains M protein. Alternatively, if the peak area ratio of κ light chain to λ light chain is less than 1.8 or greater than 3.5, and the peak shape is a non-Gaussian distribution, it is determined that the test sample contains M protein.

[0069] Among them, the problems with method 1 include: the mass spectrometry peak of the M protein light chain of weakly positive or double-positive patients is usually of low intensity, and no obvious mass spectrometry peak can be observed, which is easy to miss; the spectrum to be tested needs to be superimposed and compared with the M protein negative spectrum, which is difficult to automate; the visual interpretation results will be affected by human experience and subjective factors, and consistency cannot be guaranteed; visual interpretation is difficult to apply to large-scale screening of diseases; and quantitative information cannot be given.

[0070] Problems with method 2 include: the mass spectrometry peak of the M protein light chain of weakly positive or double-positive patients is usually of low intensity, and the mass spectrometry peak area ratio of the κ light chain to the λ light chain may be within the normal range. It is unable to identify M proteins with low peak intensity and mass spectrometry peak area ratio of the κ light chain to the λ light chain within the normal range, and has low sensitivity; machine learning is used to determine whether the mass spectrometry peak shape is a Gaussian distribution, which requires the collection of a large number of samples for training, and the process is complicated and time-consuming; quantitative information cannot be given.

[0071] In order to solve the above problems existing in the prior art, the present application provides a method for detecting M protein, an electronic device and a storage medium.

[0072] See attached Figure 2 , Figure 2 FIG. 1 is a schematic flow chart of the main steps of the method for detecting M protein according to one embodiment of the present application. Figure 2 As shown, the method for detecting the M protein in the embodiment of the present application mainly includes the following steps S201 to S203.

[0073] Step S201: obtaining mass spectrum data of a sample to be detected;

[0074] The mass spectrum data at least includes a first preset interval and a second preset interval, the first preset interval is the distribution interval of λ light chains in the mass spectrum data, and the second preset interval is the distribution interval of κ light chains in the mass spectrum data.

[0075] Step S202: Perform peak detection on the mass spectrum data to determine whether there is a mass spectrum peak in the first preset interval and the second preset interval;

[0076] Step S203: obtaining the detection result of the M protein based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval.

[0077] Based on the method described in steps S201 to S203 above, the mass spectrometry peak of the M protein light chain of weakly positive or double-positive patients can be identified. The process is simple and fast, can be automated, consumes less reagents and consumables, and the test results are not easily affected by external conditions. It is easier to achieve standardization and can be applied to large-scale clinical testing.

[0078] The above steps S201 to S203 are further explained below.

[0079] In some embodiments of the above step S201, the sample to be tested may be a serum or urine sample. Before obtaining the mass spectrometry data of the sample to be tested, the sample to be tested needs to be preprocessed, which may specifically include the following steps:

[0080] (1) diluting the sample to be tested with a buffer solution (such as deionized water, PBS solution, etc., PBS solution is selected in this application) to obtain a sample dilution solution;

[0081] (2) adding a reducing agent (such as dithiothreitol, tris(2-carboxyethyl)phosphine, etc., in this application, tris(2-carboxyethyl)phosphine) to the sample diluent, incubating at room temperature for 10 minutes with shaking to obtain a sample reduction solution;

[0082] (3) Take 5 μl of sample reduction solution, mix it with 10-20 μl of matrix solution (such as sinapinic acid, α-cyano-4-hydroxycinnamic acid, dihydroxybenzoic acid, etc., and 20 μl of sinapinic acid is selected in this application), and take 2.5 μl and apply it to the sample chip, and place it on a 40°C heating plate to heat and dry it.

[0083] After the sample to be detected is pre-processed, the sample acquisition system can be used to collect data on the processed sample to obtain mass spectrum data of the sample to be detected.

[0084] Among them, the sample collection system includes sample chips, injection systems, ion sources, flight tubes, mass detectors, digital acquisition cards, acquisition control software, etc., which are used for sample loading and data collection.

[0085] Obtaining mass spectrometry data of the sample to be tested specifically includes:

[0086] (1) Loading the chip with the sample into the MALDI-TOF mass spectrometer;

[0087] (2) The acquisition conditions of the MALDI-TOF mass spectrometer were set, including: laser energy 5-25 μJ, detector voltage 0.4-0.7 kV, focus mass 20000-25000 Da, scanning speed 0.5-1.5 mm / s, scanning range 2000-200000 Da, and the mass spectrometry analysis mode was linear positive ion mode.

[0088] Through the above implementation, mass spectrometry data of the sample to be tested can be obtained, usually a mass spectrum, that is, a spectrum formed by the mass spectrometry signals of the sample ions in the mass spectrometer. The mass spectrum can contain information such as the relative abundance, mass-to-charge ratio, and mass spectrum of different chemical ions in the sample.

[0089] Furthermore, after obtaining the mass spectrum of the sample to be tested, the mass spectrum data can be divided according to the distribution intervals of different light chains to facilitate subsequent analysis of the mass spectrum.

[0090] Specifically, the mass spectrum can be divided into a first preset interval, a second preset interval and a third preset interval according to the mass-to-charge ratio of the single-charged ions of the λ light chain, the κ light chain and the glycosylated light chain.

[0091] See attached Figure 3 , Figure 3 FIG. 1 is a schematic diagram of mass spectrum data of a sample to be detected according to an embodiment of the present application. Figure 3 As shown, the mass-to-charge ratio range of the λ light chain single-charged ion [M+H]+m / z (Da) 22200-23100 can be divided into a first preset interval (wherein, [M+H]+m / z (Da) 22200-23100 is a method of expressing an ion form, [M+H] represents the total mass of the light chain molecule plus a hydrogen atom (H), and m / z (Da) represents the mass-to-charge ratio of the ion); the mass-to-charge ratio range of the κ light chain single-charged ion [M+H]+m / z (Da) 23100-24600 is divided into a second preset interval; and the mass-to-charge ratio range of the glycosylated light chain single-charged ion [M+H]+m / z (Da) 25000-27800 is divided into a third preset interval.

[0092] It should be pointed out that the above examples of preprocessing of the samples to be tested, the acquisition conditions of the mass spectrometer, and the first to third preset intervals in the mass spectrometry data are only schematic illustrations. In actual applications, technicians in this field can make settings according to specific scenarios, and no limitation is made here.

[0093] The above is the description of step S201.

[0094] Furthermore, after obtaining the mass spectrum data of the sample to be detected, the mass spectrum data of the sample to be detected can be read into the software algorithm, and the above steps S202 to S203 are executed based on the software algorithm to analyze the mass spectrum data and obtain the detection result of the M protein.

[0095] In some implementations of step S202, see the attached Figure 4 , Figure 4 FIG. 1 is a flow chart of the main steps of performing peak detection on mass spectrum data according to an embodiment of the present application. Figure 4 As shown, it mainly includes the following steps S401 to S402:

[0096] Step S401: obtaining continuous wavelet transform peak detection parameters;

[0097] Step S402: performing continuous wavelet transform peak detection on the mass spectrum data based on the continuous wavelet transform peak parameters.

[0098] The continuous wavelet transform (CWT) peak detection method can identify peaks of different scales and amplitudes, effectively identify and separate signals, and has strong noise suppression capabilities. Within a certain scale range, the maximum CWT coefficient on the ridge can be used to estimate the peak intensity and identify strong and weak peaks. In addition, by using different wavelet scales, peaks of different widths can be detected. By adjusting the size of the wavelet scale, different response width ranges for wide peaks and narrow peaks can be achieved. Larger scales can be used to detect wide peaks, and smaller scales can be used to detect narrow peaks.

[0099] When performing peak detection on mass spectrum data, the acquired continuous wavelet transform peak detection parameters may be pre-set parameters.

[0100] In some embodiments, for narrow and sharp mass spectrometry peaks in mass spectrometry data, a smaller wavelet scale can be selected for detection to improve the sensitivity and accuracy of peak detection. For example, the minimum value of the wavelet scale range in the monitoring parameters is set to 1, the maximum value is 64, and the signal-to-noise ratio threshold is 5.

[0101] The signal-to-noise ratio refers to the ratio of the peak value to the background noise in the signal, and the signal-to-noise ratio threshold is the minimum signal-to-noise ratio used to detect the peak signal.

[0102] By performing peak detection on the mass spectrum data of the sample to be detected based on the continuous wavelet transform peak detection method, it can be determined whether there is a narrow and sharp mass spectrum peak in the first preset interval and the second preset interval, thereby determining whether the sample to be detected contains M protein.

[0103] Furthermore, if there are mass spectrum peaks in the mass spectrum data, each mass spectrum peak and its position, intensity and other information can be stored in a peak list, and mass spectrum peaks outside the first preset interval, the second preset interval and the third preset interval can be filtered out from the peak list to reduce noise and miscellaneous peaks in the mass spectrum and improve the accuracy and reliability of the detection results.

[0104] The above is a further description of step S202 , and the following is a further description of step S203 .

[0105] In some embodiments of the above step S203, the M protein can be detected based on the difference in spectral peak morphology between monoclonal immunoglobulin and polyclonal immunoglobulin.

[0106] Specifically, the mass spectrometry peaks of both the patient's M protein and the normal person's polyclonal immunoglobulin will reach a certain intensity within the first preset interval and the second preset interval, which can be used as a quality control. The mass spectrometry peaks of the M protein and the polyclonal immunoglobulin are very different in morphology. Figure 1b and Figure 1c As shown in Figure 2, the mass spectrum peak of M protein is narrow and sharp; Figure 1a As shown, the peak of the polyclonal immunoglobulin protein spectrum is broad and rounded, and presents a Gaussian distribution within the first preset interval and the second preset interval.

[0107] Therefore, if a mass spectrum peak is identified within the first preset interval and the second preset interval based on the continuous wavelet transform peak detection parameter, it can only be an M protein spectrum peak or noise. Therefore, using the ratio of its intensity to the average intensity of the polyclonal immunoglobulin spectrum peak as a screening condition can effectively filter out the noise and obtain the result. That is, when the ratio of the intensity of the identified M protein spectrum peak to the polyclonal immunoglobulin spectrum peak reaches a certain value, it can be determined that the M protein exists in the sample to be tested, thereby improving the detection sensitivity of the M protein.

[0108] Specifically, if there is no mass spectrum peak in the first preset interval and the second preset interval, it can be determined that the sample to be detected does not contain M protein.

[0109] If there are mass spectrum peaks in the first preset interval and the second preset interval, the position and intensity of each mass spectrum peak can be obtained; and the detection result of M protein is obtained based on the average intensity of the mass spectrum data and the intensity of each mass spectrum peak in the first preset interval and the second preset interval.

[0110] In some embodiments, see Appendix Figure 5 , Figure 5 1 is a flow chart of the main steps of obtaining the detection result of M protein based on the average intensity of mass spectrum data and the intensity of each mass spectrum peak in the first preset interval and the second preset interval according to an embodiment of the present application. Figure 5 As shown, it mainly includes the following steps S501 to S504:

[0111] Step S501: obtaining a ratio coefficient between the intensity of each mass spectrum peak and the average intensity of mass spectrum data within a first preset interval and a second preset interval;

[0112] First, the average intensity of the original mass spectrum data can be calculated within the first preset interval and the second preset interval, that is, an average operation is performed on all data points within the first preset interval and the second preset interval to obtain the average intensity of the mass spectrum data.

[0113] It should be pointed out that when performing mass spectrometry analysis, in order to ensure the accuracy and reliability of the test results, it is necessary to set a minimum detection threshold as the quality control threshold, such as 8E-5 (V). Under normal circumstances, the average intensity of the mass spectrometry data should be greater than or equal to the quality control threshold. If the average intensity of the mass spectrometry data is less than the quality control threshold, the M protein test result is judged to be invalid, and the test is repeated or the subsequent steps are skipped to ensure the accuracy and reliability of the test results.

[0114] Furthermore, the intensity value of each mass spectrum peak within the first preset interval and the second preset interval may be directly obtained from the peak value list.

[0115] Finally, the ratio coefficient between the intensity of each mass spectrum peak and the average intensity of the mass spectrum data is calculated.

[0116] Specifically, the intensity of each mass spectrum peak within the first preset interval and the second preset interval may be divided by the average intensity of the mass spectrum data to obtain a proportionality coefficient.

[0117] Step S502: determining whether there is at least one proportionality coefficient greater than or equal to a proportionality coefficient threshold value;

[0118] Specifically, multiple proportionality coefficients can be compared with the proportionality coefficient threshold value to determine whether there is at least one proportionality coefficient greater than or equal to the proportionality coefficient threshold value, and mass spectrum peaks with proportionality coefficients less than the proportionality coefficient threshold value can be filtered out from the peak list.

[0119] The proportionality coefficient threshold is a threshold value set by comparing the ratio of the mass spectrum peak area to the noise. When the ratio of the mass spectrum peak area to the noise exceeds the set proportionality coefficient threshold, the mass spectrum peak is considered to be a real signal, otherwise it is considered to be noise.

[0120] In the present application, the proportional coefficient threshold value can be set to 1.0, which can effectively distinguish noise from M protein and filter out the noise while having relatively high sensitivity.

[0121] In practical applications, those skilled in the art may appropriately adjust the value of the proportional coefficient threshold according to the requirements of the specific scenario on sensitivity and noise filtering effect, so as to achieve better detection effect.

[0122] Further, within the first preset interval and the second preset interval, if there is at least one proportionality coefficient greater than or equal to the proportionality coefficient threshold, step S503 is executed, otherwise step S504 is executed.

[0123] Step S503: determining whether the sample to be tested contains M protein;

[0124] Step S504: Determine that the sample to be tested does not contain M protein.

[0125] Among them, the mass spectrum peak whose proportionality coefficient is greater than or equal to the proportionality coefficient threshold value is the M protein spectrum peak. Therefore, if there is at least one proportionality coefficient greater than or equal to the proportionality coefficient threshold value, it can be determined that the sample to be tested contains M protein; if there is no proportionality coefficient greater than or equal to the proportionality coefficient threshold value, it can be determined that the sample to be tested does not contain M protein.

[0126] Through the above implementation, the signal can be effectively identified and separated from the noise, ensuring the high sensitivity of the M protein detection method.

[0127] Furthermore, in some embodiments, after the M protein spectral peak is detected, there may be several sub-peaks on the right side of the main peak of the M protein spectrum (higher mass-to-charge ratio range). The formation of the sub-peaks is due to the addition of the M protein with other substances (such as the matrix) during the ionization process. Only when the intensity of the M protein main peak is very large will an observable sub-peak be formed.

[0128] See attached Figure 6 , Figure 6 FIG. 1 is a schematic diagram of a sub-peak of a M protein spectrum peak according to an embodiment of the present application. Figure 6 As shown, there are three sub-peaks on the right side of the main peak of the M protein spectrum in the first preset interval and in the second preset interval.

[0129] Furthermore, in order to avoid the influence of sub-peaks on M protein typing or glycosylation discrimination, the sub-peaks of the M protein spectrum peak can be removed from the peak detection result, and the type of M protein in the sample to be detected can be obtained based on the remaining M protein spectrum peak.

[0130] Among them, the methods for removing sub-peaks of the M protein spectrum peak generally include the following two methods:

[0131] Method 1:

[0132] a. If there is a mass spectrum peak in the peak list within the first preset interval and the second preset interval, find the mass spectrum peak with the largest peak intensity as the main peak of the M protein spectrum peak; if it does not exist, skip the step of removing the sub-peak of the M protein spectrum peak;

[0133] b. The first sub-peak is preset to be located within 216.1Da±20.0Da to the right of the main peak, and the intensity range is 0.06–0.18 times the intensity of the main peak;

[0134] c. The second sub-peak is preset to be located within 436.1Da±20.0Da to the right of the main peak, and the intensity range is 0.005–0.1 times the intensity of the main peak;

[0135] d. The third sub-peak is assumed to be located within 777.8Da±20.0Da to the right of the main peak, and the intensity range is 0.015–0.06 times the intensity of the main peak;

[0136] e. If the first sub-peak, the second sub-peak and the third sub-peak mentioned above exist in the peak list, they will all be filtered out.

[0137] Method 2:

[0138] a. If there is a mass spectrum peak in the peak list within the first preset interval and the second preset interval, find the first mass spectrum peak as the main peak of the M protein spectrum peak; if not, skip the step of removing the sub-peak of the M protein spectrum peak;

[0139] b. The first sub-peak is preset to be located within 216.1Da±20.0Da on the right side of the main peak, and the intensity range is 0.06–0.18 times the intensity of the main peak.

[0140] c. The second sub-peak is preset to be located within 436.1Da±20.0Da on the right side of the main peak, and the intensity range is 0.005–0.1 times the intensity of the main peak.

[0141] d. The third sub-peak is preset to be located within 777.8Da±20.0Da on the right side of the main peak, and its intensity range is 0.015–0.06 times that of the main peak.

[0142] e. If the first sub-peak, the second sub-peak and the third sub-peak mentioned above exist in the peak list, filter them all out;

[0143] f. If there is a mass spectrum peak on the right side of the main peak in the peak list, take it as the main peak; otherwise, end;

[0144] g. Repeat steps b-f until the end of the peak list.

[0145] Through the above implementation, the sub-peak of the M protein spectrum peak can be removed in the peak detection result, effectively eliminating the influence of the M protein spectrum sub-peak on the M protein typing or glycosylation discrimination, avoiding subsequent typing errors or glycosylation identification errors.

[0146] Furthermore, the type of M protein in the sample to be tested can be obtained based on the remaining M protein spectrum peak.

[0147] Among them, the types of M protein include λ-type M protein positive, κ-type M protein positive, and λ-type and κ-type M protein double positive.

[0148] Specifically, if there is an M protein spectrum peak in the first preset interval, the type of M protein in the sample to be tested is determined to be λ-type M protein positive;

[0149] If there is an M protein spectrum peak in the second preset interval, the type of M protein in the sample to be tested is determined to be κ-type M protein positive;

[0150] If the M protein spectrum peaks exist in both the first preset interval and the second preset interval, it is determined that the type of M protein in the sample to be detected is double positive for λ-type and κ-type M protein.

[0151] The above is the description of step S203.

[0152] Furthermore, in some embodiments, when the sample to be detected contains M protein, the quantitative result of M protein can be obtained based on the area of ​​the M protein spectrum peak.

[0153] Specifically, the steps include:

[0154] (1) According to the type of M protein in the sample to be detected, select the maximum peak within the first preset interval and / or the second preset interval in the peak list, and calculate its area;

[0155] Specifically, if the type of M protein in the sample to be tested is λ-type M protein positive, the maximum peak value within the first preset interval is obtained and its area is calculated; if the type of M protein in the sample to be tested is κ-type M protein positive, the maximum peak value within the second preset interval is obtained and its area is calculated; if the type of M protein in the sample to be tested is λ-type and κ-type M protein double positive, the maximum peak values ​​in the first preset interval and the second preset interval are obtained respectively, and their areas are calculated respectively.

[0156] (2) calculating the total area of ​​the first preset interval and the second preset interval;

[0157] (3) Calculate the ratio of the maximum peak area to the total area as the quantitative result of M protein.

[0158] Specifically, if the type of M protein in the sample to be tested is λ-type M protein positive, the quantitative result of λ-type M protein will be obtained; if the type of M protein in the sample to be tested is κ-type M protein positive, the quantitative result of κ-type M protein will be obtained; if the type of M protein in the sample to be tested is λ-type and κ-type M protein double positive, two quantitative results will be obtained, namely the quantitative results of λ-type M protein and κ-type M protein.

[0159] In some embodiments, see Appendix Figure 7a , Figure 7b and Figure 7c , Figure 7a is a schematic diagram of the positive qualitative result and quantitative result of λ-type M protein according to one embodiment of the present application, Figure 7b is a schematic diagram of the qualitative result and quantitative result of λ-type M protein positive (weak positive) according to another embodiment of the present application, Figure 7c It is a schematic diagram of the positive qualitative result and quantitative result of κ-type M protein according to one embodiment of the present application.

[0160] The above is an explanation of obtaining the quantitative results of M protein.

[0161] In some embodiments, the method for detecting M protein provided in the present application further comprises: performing glycosylation detection on the third preset interval to obtain a glycosylation detection result.

[0162] Specifically, a preset continuous wavelet transform peak detection parameter may be obtained to perform peak detection on the mass spectrum data to determine whether a mass spectrum peak exists within the third preset interval.

[0163] The mass spectrum peak of the glycosylated light chain is usually a cluster of mass spectrum peaks composed of multiple peaks, and the intensity is relatively large. Therefore, to determine whether the M protein is glycosylated positive, it is necessary to meet the condition that there are multiple (such as 3 or more) mass spectrum peaks in the glycosylation area (i.e., the third preset interval). Therefore, peak detection is performed on the mass spectrum data to determine whether there is a mass spectrum peak that meets the above conditions in the third preset interval.

[0164] Further, if there are mass spectrum peaks within the third preset interval, the number of mass spectrum peaks whose signal-to-noise ratio values ​​are greater than or equal to the signal-to-noise ratio threshold value is obtained.

[0165] Among them, the signal-to-noise ratio refers to the ratio of the peak value in the signal to the background noise. The higher the signal-to-noise ratio, the stronger the signal intensity, the smaller the impact of noise, the clearer and more obvious the mass-to-charge ratio peak on the mass spectrum, and the easier it is to identify and interpret.

[0166] When performing glycosylation detection on the mass spectrometry data of the sample to be detected, a signal-to-noise ratio threshold value, such as 30, can be preset, and the number of mass spectrometry peaks with a signal-to-noise ratio value greater than or equal to the signal-to-noise ratio threshold value can be obtained.

[0167] Furthermore, when the number of mass spectrum peaks reaches a preset number, such as 3, the sample to be tested can be determined to be M protein glycosylation positive.

[0168] That is, within the third preset interval, if there is a cluster of mass spectrum peaks consisting of multiple peaks and the intensity is relatively large, the sample to be tested is determined to be M protein glycosylation positive, otherwise the sample to be tested is determined to be M protein glycosylation negative.

[0169] On the other hand, if there is no mass spectrum peak within the third preset interval, the sample to be tested is directly determined to be M protein glycosylation negative.

[0170] Among them, positive M protein glycosylation usually refers to the detection of glycosylation modification on immunoglobulin. Glycosylation modification is a common protein modification method. It refers to the addition of sugar groups, usually glucose molecules, to protein molecules to change the structure, stability, activity or solubility of proteins. Glycosylation modification plays an important regulatory role in organisms and can affect protein stability, folding, solubility, antigenicity, cell adhesion and other properties, thereby participating in a variety of physiological and pathological processes. In clinical practice, changes in glycosylation modification can be used to screen for certain diseases, such as tumors and cardiovascular diseases.

[0171] In some embodiments, see Appendix Figure 8 , Figure 8It is a schematic diagram of the positive qualitative result of M protein glycosylation according to one embodiment of the present application.

[0172] The M protein detection method provided in the present application can identify the M protein light chain mass spectrometry peak of weakly positive or double-positive patients. The process is simple and fast, can be automated, consumes less reagents and consumables, and the detection results are not easily affected by external conditions. It is easier to achieve standardization and can be applied to large-scale clinical testing.

[0173] In addition, there is no need for spectral superposition and comparison, and the interpretation does not depend on the mass spectrometry peak area ratio of κ light chain and λ light chain, which solves the shortcomings of existing methods such as low sensitivity and high missed detection rate of weakly positive or double-positive patients with κ light chain and λ light chain; no machine learning is required, which solves the shortcomings of existing methods such as high complexity and long time consumption; it is easy to automate, which solves the shortcomings of poor consistency of manual interpretation and difficulty in large-scale promotion.

[0174] Furthermore, the sample to be tested can be subjected to quantitative detection of M protein and qualitative detection of immunoglobulin light chain glycosylation, thereby achieving high-throughput, high-sensitivity, and low-cost qualitative and quantitative detection of M protein and qualitative detection of immunoglobulin light chain glycosylation.

[0175] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0176] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0177] Furthermore, the present application also provides an electronic device. Fig. 9 , Fig. 9 Schematic diagram of the main structure of an electronic device according to an embodiment of the present application. Fig. 9As shown, the electronic device in the embodiment of the present application mainly includes a processor 901 and a storage device 902. The storage device 902 can be configured to store a program for executing the detection method of the M protein of the above method embodiment, and the processor 901 can be configured to execute the program in the storage device 902, which includes but is not limited to the program for executing the detection method of the M protein of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present application is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application.

[0178] In some possible implementations of the present application, the electronic device may include multiple processors 901 and multiple storage devices 902. The program for executing the method for detecting the M protein of the above method embodiment can be divided into multiple subprograms, and each subprogram can be loaded and run by the processor 901 to execute different steps of the method for detecting the M protein of the above method embodiment. Specifically, each subprogram can be stored in different storage devices 902, and each processor 901 can be configured to execute the program in one or more storage devices 902 to jointly implement the method for detecting the M protein of the above method embodiment, that is, each processor 901 executes different steps of the method for detecting the M protein of the above method embodiment, to jointly implement the method for detecting the M protein of the above method embodiment.

[0179] The above-mentioned multiple processors 901 may be processors deployed on the same device. For example, the above-mentioned electronic device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors 901 may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors 901 may also be processors deployed on different devices. For example, the above-mentioned electronic device may be a server cluster, and the above-mentioned multiple processors 901 may be processors on different servers in the server cluster.

[0180] Further, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium may be configured to store a program for performing the detection method of the M protein of the above-mentioned method embodiment, and the program may be loaded and run by the processor to implement the above-mentioned detection method of the M protein. For ease of explanation, only the parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present application. The computer-readable storage medium may be a storage device including various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0181] It should be noted that the relevant user personal information that may be involved in the various embodiments of the present application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0182] The user personal information processed by this application will vary depending on the specific product / service scenario, and shall be based on the specific scenario in which the user uses the product / service, and may involve the user's account information, device information, driving information, vehicle information or other related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0183] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that meet industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0184] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for detecting M protein, characterized in that: The method comprises: Acquire mass spectrum data of the sample to be detected; the mass spectrum data at least includes a first preset interval and a second preset interval, the first preset interval is a distribution interval of λ light chain in the mass spectrum data, and the second preset interval is a distribution interval of κ light chain in the mass spectrum data; Performing peak detection on the mass spectrum data to determine whether there is a mass spectrum peak in the first preset interval and the second preset interval; The detection result of the M protein is obtained based on the peak detection result and / or the average intensity of the mass spectrum data in the first preset interval and the second preset interval; wherein, If the mass spectrum peak does not exist in the first preset interval and the second preset interval, it is determined that the sample to be detected does not contain M protein; If the mass spectrum peak exists in the first preset interval and the second preset interval, the position and intensity of each mass spectrum peak are obtained; and the proportionality coefficient between the intensity of each mass spectrum peak and the average intensity of the mass spectrum data in the first preset interval and the second preset interval is obtained; if at least one of the proportionality coefficients is greater than or equal to the proportionality coefficient threshold value, it is determined that the sample to be detected contains M protein; Among them, within the first preset interval and the second preset interval, the average intensity of the mass spectrometry data represents the average intensity of the polyclonal immunoglobulin protein spectrum peak.

2. The method for detecting M protein according to claim 1, characterized in that: The method further comprises: Determine a mass spectrum peak whose proportionality coefficient is greater than or equal to the proportionality coefficient threshold value as an M protein spectrum peak; Removing the sub-peak of the M protein spectrum peak in the peak detection result, and obtaining the type of M protein in the sample to be detected based on the remaining M protein spectrum peak; The types of M protein include λ-type M protein positive, κ-type M protein positive, and λ-type and κ-type M protein double positive.

3. The method for detecting M protein according to claim 2, characterized in that: The method further comprises: When the sample to be detected contains the M protein, a quantitative result of the M protein is obtained based on the area of ​​the M protein spectrum peak.

4. The method for detecting M protein according to claim 1, characterized in that: The mass spectrum data further includes a third preset interval, and the third preset interval is a distribution interval of glycosylated light chains in the mass spectrum data; the method further includes: Perform glycosylation detection on the third preset interval to obtain a glycosylation detection result.

5. The method for detecting M protein according to claim 4, characterized in that: The performing glycosylation detection on the third preset interval to obtain a glycosylation detection result comprises: Performing peak detection on the mass spectrum data to determine whether there is a mass spectrum peak within the third preset interval; If so, obtaining the number of mass spectrum peaks whose signal-to-noise ratio value is greater than or equal to the signal-to-noise ratio threshold value within the third preset interval; when the number of mass spectrum peaks reaches the preset number, determining that the sample to be tested is M protein glycosylation positive, otherwise determining that the sample to be tested is M protein glycosylation negative; If not present, the sample to be tested is determined to be negative for glycosylation of the M protein.

6. The method for detecting M protein according to claim 1 or 5, characterized in that: The peak detection of the mass spectrum data comprises: Obtain continuous wavelet transform peak detection parameters; Continuous wavelet transform peak detection is performed on the mass spectrum data based on the continuous wavelet transform peak detection parameters.

7. An electronic device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the method for detecting the M protein according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the method for detecting the M protein according to any one of claims 1 to 6.

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