Complex amplification system for body fluid tracing based on 31 mRNA markers and its application

Abstract

The application discloses a composite amplification detection system for body fluid tracing based on mRNA markers and application thereof, high-specificity mRNA markers can realize accurate tracing of five main body fluid types including peripheral blood, saliva, semen, menstrual blood and vaginal secretion, and can make up for the problem that in the previous body fluid tracing system, less markers lead to false positive or false negative problems caused by cross-reaction, and effectively improve the accuracy of single source and mixed body fluid, and old body fluid identification, and improve the applicability and feasibility of the system in forensic practice and biological research. Based on the fluorescence labeling technology, the application realizes stable, specific and efficient composite amplification reaction of multiple high-specificity mRNA markers at the same time, and the number of composite mRNA markers in the system is relatively large, and the identification efficiency of single source and mixed body fluid, and old body fluid is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological detection, in particular to a composite amplification system for body fluid tracing based on 31 mRNA markers and application thereof. BACKGROUND

[0002] Body fluid stain samples at the crime scene are important forensic biological evidence, and accurate identification of the scene evidence helps to reconstruct the crime scene. For example, in sexual assault cases, distinguishing between semen, saliva or vaginal secretions and other body fluids is crucial to confirm the nature of the crime. With the rapid development of molecular biology technology, based on the differences in the expression levels of RNA such as messenger RNA (mRNA), microRNA (miRNA) and circular RNA (circRNA) from different body fluid stains, forensic microbiological 16S rRNA gene detection and DNA methylation level detection, these methods are gradually becoming new tools for forensic body fluid stain identification. Among these methods, mRNA expression pattern analysis with high sensitivity and strong specificity is considered a promising method for forensic body fluid identification.

[0003] Bauer et al. first pioneered the proof that epithelial (especially endometrial) cell-specific mRNA markers could be used for the identification of menstrual blood. This pioneering study laid a solid foundation for the application of mRNA in forensic body fluid identification. Subsequently, more and more mRNA markers have been proven to have high body fluid specificity, making mRNA analysis increasingly widely used in the field of forensic body fluid identification. The European DNA Profiling Group (EDNAP) and the Forensic RNA Profiling Group (FoRNAP) systematically evaluated the identification performance of these mRNA markers in low template amounts or simulated case samples by capillary electrophoresis (CE) or massively parallel sequencing (MPS) technology. These evaluation results show that mRNA analysis has broad application prospects in forensic genetics research.

[0004] RNA typing based on reverse transcription-polymerase chain reaction (RT-PCR) technology and CE platform has high sensitivity and strong specificity. Detecting specific molecular markers of multiple different tissue-derived body fluids in the same composite amplification reaction is considered an effective means of identifying unknown samples at crime scenes. Some forensic laboratories have developed mRNA composite detection systems suitable for multiple body fluid types based on CE platforms, and these systems have been applied to actual cases, providing strong support for forensic body fluid identification. However, current research still has a certain distance to achieve accurate identification of forensic body fluid biological evidence. First, using fewer markers to determine the source of a body fluid may produce false positives or false negatives due to cross-reactions, making it difficult to provide more accurate identification information for body fluid samples found at crime scenes; second, biological samples commonly found at crime scenes may be a mixture of different types of body fluids, such as a mixture of semen, saliva, or vaginal secretions in sexual assault cases, and existing research is difficult to identify and distinguish mixed samples.

[0005] Currently, constructing a body fluid tracing system containing more high-body fluid-specific mRNA markers is one of the key points of forensic body fluid identification research, which puts higher requirements on the development technology of composite amplification system and evidence analysis capability. The development of six-color fluorescent labeling technology enables the composite detection system to cover a larger number of mRNA markers, thereby enabling the system to provide more abundant biological information and higher body fluid tracing efficiency. However, with the increase in the number of mRNA markers in the composite detection system, the relative balance between markers becomes difficult to control due to competition effects during the reaction process, and the fine requirements for detection conditions also increase. Therefore, it is necessary to verify the amplification parameters multiple times and constantly adjust the final concentration, ratio, and combination of primers to optimize the amplification balance of each mRNA marker. In addition, machine learning, as the mainstream of intelligent data processing technology and an important means of artificial intelligence, its practical application in the medical field is an important topic at present. Through data acquisition and feature analysis of a large number of different human body fluid samples, a machine learning prediction model based on the numerical results obtained by the composite amplification system typing detection is constructed, providing a new technical support, detection platform, and artificial intelligence prediction model for the accurate identification of forensic body fluid stains, especially degraded or mixed body fluid samples.

[0006] Therefore, how to successfully develop a precise mRNA single-source and mixed body fluid tracing system based on multiple mRNA body fluid-specific molecular markers, combined with six-color fluorescent labeling technology and machine learning algorithms, has significant practical significance for daily forensic case work. SUMMARY

[0007] The purpose of the present application is to construct a plurality of mRNA marker composite typing detection system based on RT-PCR technology and CE platform, and to establish a prediction model based on typing numerical value by using machine learning algorithm, to provide accurate detection scheme and comprehensive evidence analysis process for forensic common body fluid tracing, and identification of difficult and complex body fluid samples such as degradation, aging and mixing in judicial practice.

[0008] In order to achieve the above purpose, the technical scheme adopted by the present application is:

[0009] The first aspect of the present application provides the application of mRNA markers or reagents for detecting mRNA markers in any of the following aspects:

[0010] (1) body fluid tracing;

[0011] (2) preparation of products for body fluid tracing;

[0012] (3) identification of body fluid samples;

[0013] (4) preparation of products for identification of body fluid samples;

[0014] The mRNA markers include peripheral blood specific mRNA markers, semen specific mRNA markers, saliva specific mRNA markers, menstrual blood specific mRNA markers, vaginal secretion specific mRNA markers, male specific mRNA markers and female specific mRNA markers;

[0015] The peripheral blood specific mRNA markers include SPTB, PPBP, ALAS2, HBD, HBB and HBA1;

[0016] The semen specific mRNA markers include SEMG1, MSMB, PRM2 and TGM4;

[0017] The saliva specific mRNA markers include STATH, HTN3, PRH2, FDCSP, MUC7 and SMR3B;

[0018] The menstrual blood specific mRNA markers include STC1, MMP3, MMP7, PAEP and MMP10;

[0019] The vaginal secretion specific mRNA markers include MUC4, DKK4, FUT6, SFTA2 and CYP2B7P1;

[0020] The male specific mRNA markers include RPS4Y1;

[0021] The female specific mRNA markers include XIST.

[0022] The second aspect of the present application provides a kit comprising reagents for detecting the mRNA markers of the first aspect, the reagents including detection reagents for detecting the mRNA markers.

[0023] In some embodiments, the detection reagents for detecting the mRNA markers include primers for amplifying the mRNA markers.

[0024] In some embodiments, the detection reagents for detecting the mRNA markers further include primers for amplifying housekeeping genes. Housekeeping genes are a class of genes that are always kept at a low level of methylation and are always in an active transcription state, are highly conserved and are expressed continuously in most cases. Their expression levels are less affected by environmental factors. When detecting mRNA markers, using them as a reference ensures the accuracy of the detection results.

[0025] In some embodiments, the housekeeping genes include B2M, ACTB and GAPDH.

[0026] In some embodiments, the primers for amplifying the mRNA markers and the primers for amplifying housekeeping genes are labeled with fluorescent dyes, the primers for amplifying peripheral blood-specific mRNA markers, semen-specific mRNA markers, saliva-specific mRNA markers, menstrual blood-specific mRNA markers, vaginal secretion-specific mRNA markers are labeled with different fluorescent dyes, and the primers for amplifying male-specific mRNA markers and female-specific mRNA markers are labeled with different fluorescent dyes.

[0027] In some embodiments, the primers for amplifying the peripheral blood-specific mRNA markers are labeled with fluorescent dye A, the primers for amplifying the semen-specific mRNA markers are labeled with fluorescent dye B, the primers for amplifying the saliva-specific mRNA markers are labeled with fluorescent dye C, the primers for amplifying the menstrual blood-specific mRNA markers are labeled with fluorescent dye D, the primers for amplifying the vaginal secretion-specific mRNA markers are labeled with fluorescent dye E, the fluorescent dyes A-E are different, the primers for amplifying the male-specific mRNA markers and the primers for amplifying the female-specific mRNA markers are labeled with any one of the fluorescent dyes A-E, respectively, the fluorescent dye used to label the primers for amplifying the male-specific mRNA markers is different from the fluorescent dye used to label the primers for amplifying the female-specific mRNA markers, and the primers for amplifying the housekeeping genes are labeled with any one of the fluorescent dyes A-E. At least one of the primers in any pair of primers for amplifying mRNA markers is labeled with a fluorescent dye.

[0028] In some embodiments, the fluorescent dyes A-E are selected from any one of 6-FAM, HEX, TET, ROX, TAMRA, JOE, Cy3, Cy5, L592.

[0029] In some embodiments, the detection reagent for detecting the mRNA marker comprises the nucleotide sequence shown in SEQ ID NO: 1-62. The primer sequences for amplifying HTN3 are shown in SEQ ID NO. 1-2; the primer sequences for amplifying PRH2 are shown in SEQ ID NO. 3-4; the primer sequences for amplifying FDCSP are shown in SEQ ID NO. 5-6; the primer sequences for amplifying STATH are shown in SEQ ID NO. 7-8; the primer sequences for amplifying SMR3B are shown in SEQ ID NO. 9-10; the primer sequences for amplifying MUC7 are shown in SEQ ID NO. 11-12; the primer sequences for amplifying ACTB are shown in SEQ ID NO. 13-14; the primer sequences for amplifying GAPDH are shown in SEQ ID NO. 15-16; the primer sequences for amplifying B2M are shown in SEQ ID NO. 17-18; the primer sequences for amplifying SEMG1 are shown in SEQ ID NO. 19-20; the primer sequences for amplifying MSMB are shown in SEQ ID NO. 21-22; the primer sequences for amplifying PRM2 are shown in SEQ ID NO. 23-24; and the primer sequences for amplifying TGM4 are shown in SEQ ID NO. 1-20. Primer sequences for amplifying RPS4Y1 are shown in SEQ ID NO. 25-26; primer sequences for amplifying SPTB are shown in SEQ ID NO. 29-30; primer sequences for amplifying PPBP are shown in SEQ ID NO. 31-32; primer sequences for amplifying HBB are shown in SEQ ID NO. 33-34; primer sequences for amplifying ALAS2 are shown in SEQ ID NO. 35-36; primer sequences for amplifying HBA1 are shown in SEQ ID NO. 37-38; primer sequences for amplifying HBD are shown in SEQ ID NO. 39-40; primer sequences for amplifying STC1 are shown in SEQ ID NO. 41-42; primer sequences for amplifying MMP3 are shown in SEQ ID NO. 43-44; primer sequences for amplifying MMP7 are shown in SEQ ID NO. 45-46; primer sequences for amplifying PAEP are shown in SEQ ID NO. 47-48; primer sequences for amplifying MMP10 are shown in SEQ ID NO. 45-46. Primer sequences for amplifying XIST are shown in SEQ ID NO. 49-50; primer sequences for amplifying MUC4 are shown in SEQ ID NO. 53-54; primer sequences for amplifying DKK4 are shown in SEQ ID NO. 55-56; primer sequences for amplifying FUT6 are shown in SEQ ID NO. 57-58; primer sequences for amplifying SFTA2 are shown in SEQ ID NO. 59-60; and primer sequences for amplifying CYP2B7P1 are shown in SEQ ID NO. 61-62.

[0030] In some embodiments, the first set of primers (SEQ ID NO.1 to SEQ ID NO.18) is labeled with 6-FAM fluorescent dye, the second set of primers (SEQ ID NO.19 to SEQ ID NO.28) is labeled with HEX fluorescent dye, the third set of primers (SEQ ID NO.29 to SEQ ID NO.40) is labeled with TAMRA fluorescent dye, the fourth set of primers (SEQ ID NO.41 to SEQ ID NO.52) is labeled with ROX fluorescent dye, and the fifth set of primers (SEQ ID NO.53 to SEQ ID NO.62) is labeled with L592 fluorescent dye.

[0031] A third aspect of the present invention provides a method for detecting the mRNA marker of the first aspect, comprising performing multiplex PCR using a kit of the second aspect.

[0032] In some embodiments, the composite PCR system contains 20% primer mixture and 10% template solution by volume. The primer mixture contains primers with concentrations of 0.10 μM (SEQ ID NO. 1-2), 0.18 μM (SEQ ID NO. 3-4), 0.17 μM (SEQ ID NO. 5-6), 0.72 μM (SEQ ID NO. 7-8), 0.64 μM (SEQ ID NO. 9-10), 1.15 μM (SEQ ID NO. 11-12), 0.11 μM (SEQ ID NO. 13-14), 0.38 μM (SEQ ID NO. 15-16), 0.06 μM (SEQ ID NO. 17-18), 0.12 μM (SEQ ID NO. 19-20), and 0.10 μM (SEQ ID NO. 21-22). The primer concentrations shown in SEQ ID NO. 23-24 are 0.12 μM, those in SEQ ID NO. 25-26 are 0.50 μM, those in SEQ ID NO. 27-28 are 0.10 μM, those in SEQ ID NO. 29-30 are 1.30 μM, those in SEQ ID NO. 31-32 are 0.17 μM, those in SEQ ID NO. 33-34 are 0.02 μM, those in SEQ ID NO. 35-36 are 0.26 μM, those in SEQ ID NO. 37-38 are 0.10 μM, those in SEQ ID NO. 39-40 are 1.60 μM, those in SEQ ID NO. 41-42 are 0.49 μM, and those in SEQ ID NO. 43-44 are 0.25 μM. The primer concentrations shown in SEQ ID NO. 45-46 are 1.28 μM, those in SEQ ID NO. 47-48 are 0.31 μM, those in SEQ ID NO. 49-50 are 0.37 μM, those in SEQ ID NO. 51-52 are 1.08 μM, those in SEQ ID NO. 53-54 are 0.56 μM, those in SEQ ID NO. 55-56 are 1.00 μM, those in SEQ ID NO. 57-58 are 1.00 μM, those in SEQ ID NO. 59-60 are 0.64 μM, and those in SEQ ID NO. 61-62 are 0.51 μM.

[0033] In some embodiments, the multiplex PCR amplification conditions are: 95°C pre-denaturation for 2 min; 94°C for 30 s, 60°C for 90 s, 72°C for 50 s, for 30 cycles; final extension at 72°C for 10 min; and the reaction mixture is kept at 4°C until removed from the thermal cycler.

[0034] In some embodiments, the method further includes detecting the composite PCR amplification products using capillary electrophoresis.

[0035] The fourth aspect of the present invention provides a method for constructing a body fluid detection prediction model, including obtaining the genotyping values ​​of mRNA markers in different body fluid samples, using the sample genotyping data for machine learning training, and obtaining a body fluid detection prediction model.

[0036] The mRNA markers include the mRNA markers of the first aspect;

[0037] The bodily fluid samples include peripheral blood, saliva, semen, menstrual blood, and vaginal secretions.

[0038] In some implementations, the prediction model includes any one of random forest, neural network, support vector machine, and CatBoost prediction models.

[0039] In some implementations, the prediction model is any one of a neural network, a support vector machine, and a CatBoost prediction model.

[0040] The beneficial effects of this invention are:

[0041] The highly specific mRNA markers provided by this invention enable precise tracing of five major body fluid types, including peripheral blood, saliva, semen, menstrual blood, and vaginal secretions. This overcomes the problem of insufficient markers in previous body fluid tracing systems, which resulted in the inability to interpret false positives or false negatives caused by cross-reactions. Furthermore, it effectively improves the accuracy of identifying single-source, mixed, and old body fluids, thereby enhancing the applicability and feasibility of the system in forensic practice and biological research.

[0042] This invention is based on fluorescent labeling technology and achieves stable, specific and efficient multiplex amplification reactions of multiple highly specific mRNA labels simultaneously. The system contains a large number of multiple mRNA labels and has high efficiency in identifying single-source and mixed body fluids as well as old body fluids. Attached Figure Description

[0043] Figure 1 This is a diagram showing the arrangement of 31 mRNA markers in the multiplex amplification detection system of Example 1;

[0044] Figure 2 The electrophoretic analysis pattern of the saliva sample in Example 2;

[0045] Figure 3 The electrophoretic analysis pattern of the semen sample in Example 2;

[0046] Figure 4 This is the electrophoretic analysis pattern of the peripheral blood sample in Example 2;

[0047] Figure 5 This is the electrophoretic analysis pattern of the menstrual blood sample from Example 2;

[0048] Figure 6 This is the electrophoretic analysis pattern of the vaginal secretion sample from Example 2;

[0049] Figure 7 The following is a graph showing the results of the body fluid identification performance evaluation of the system in Example 3: (A) PCA dimensionality reduction visualization results of 127 samples, (B) t-SNE dimensionality reduction visualization results of 127 samples. Peripheral blood, oral swabs, menstrual blood, saliva, semen and vaginal secretions are marked with red, green, purple, blue, yellow and pink, respectively.

[0050] Figure 8 The sensitivity detection results of the system in Example 4 are shown below; (AE) The average peak height of mRNA in five body fluids when different RNA templates were detected by the multiplex mRNA system; (F) Two sex markers in different body fluids with different RNA templates; (GI) The average peak height of three housekeeping genes.

[0051] Figure 9 The following are the prediction results of the body fluid identification model in Example 5: (A) Confusion matrix of the support vector machine body fluid tracing model constructed based on 127 samples and 46 mixed samples; (B) Confusion matrix of the neural network body fluid tracing model constructed based on 127 samples and 46 mixed samples; (C) Confusion matrix of the CatBoost body fluid tracing model constructed based on 127 samples and 46 mixed samples; (D) Confusion matrix of the random forest body fluid tracing model constructed based on 127 samples and 46 mixed samples. Detailed Implementation

[0052] The present invention will be further described in detail below through specific embodiments. Unless otherwise specified, the reagents used in the embodiments can be obtained from conventional commercial sources or by existing technical methods. Unless otherwise specified, the experimental or testing methods are conventional methods in the art.

[0053] Example 1: mRNA marker selection and primer design for multiplex amplification systems

[0054] (1) Selection of 31 mRNA markers

[0055] Based on the GTEx database and literature review, a comprehensive screening of body fluid-specific mRNA markers was conducted. First, transcriptome data from five body fluid-derived tissues (blood, salivary glands, uterus, vagina, and testis) were downloaded from the GTEx database. Differential expression analysis of the transcriptome data from these five body fluid tissues was performed using edgeR. The logFC differences in expression levels between the two groups were ranked, and the top 100 genes with the highest body fluid tissue specificity were selected. Subsequently, a machine learning feature selection algorithm was used to select 50 candidate genes. In addition, previously reported mRNA markers with strong body fluid specificity were included in subsequent analyses through literature review. Further validation experiments were conducted on the initially selected specific markers to ensure their specificity and stability in different body fluid samples, ultimately determining a set of 31 mRNA markers.

[0056] The body fluid-specific mRNA markers obtained according to the screening criteria were as follows: peripheral blood-specific markers SPTB, PPBP, ALAS2, HBD, HBB, and HBA1; semen-specific markers SEMG1, MSMB, PRM2, and TGM4; saliva-specific markers STATH, HTN3, PRH2, FDCSP, MUC7, and SMR3B; menstrual blood-specific markers STC1, MMP3, MMP7, PAEP, and MMP10; and vaginal secretion-specific markers MUC4, DKK4, FUT6, SFTA2, and CYP2B7P1. In addition to the above body fluid-specific mRNA markers, one male-specific mRNA marker (RPS4Y1) and one female-specific mRNA marker (XIST) were added. Housekeeping genes B2M, ACTB, and GAPDH were selected. Samples with more than one housekeeping gene detected were included in subsequent analyses.

[0057] (2) Determination of mRNA marker primer sequences

[0058] To ensure primer specificity and compatibility, primers were designed for each target gene. Forward or reverse primers for mRNA markers were positioned at exon-exon junctions or contained at least one intron to avoid DNA products. The corresponding transcriptome product sequences were located from the NCBI database based on the gene name. Primer-BLAST was used for primer design and validation to ensure that only one mRNA amplification product was generated, and no amplification product was generated in the genomic DNA. Basic primer design principles were followed: ① Amplicon size was controlled within 300 bp; ② GC content of primer sequences was 40-60%; ③ The annealing temperature (Tm) of each primer pair was close to 59℃, and the difference in Tm values ​​between primers did not exceed 3℃; ④ The primer sequences themselves did not form hairpin structures, primer dimers, etc. Multiplex Manager software was used to detect any interference between different primers at multiple sites.

[0059] Based on the above principles and methods, the sequences of the specific amplification primers for 31 mRNA markers are as follows: HTN3: SEQ ID NO. 1-2; PRH2: SEQ ID NO. 3-4; FDCSP: SEQ ID NO. 5-6; STATH: SEQ ID NO. 7-8; SMR3B: SEQ ID NO. 9-10; MUC7: SEQ ID NO. 11-12; ACTB: SEQ ID NO. 13-14; GAPDH: SEQ ID NO. 15-16; B2M: SEQ ID NO. 17-18; SEMG1: SEQ ID NO. 19-20; MSMB: SEQ ID NO. 21-22; PRM2: SEQ ID NO. 23-24; TGM4: SEQ ID NO. 25-26; RPS4Y1: SEQ ID NO. 27-28; SPTB: SEQ ID NO. 29-30; PPBP: SEQ ID NO. 31-32; HBB: SEQ ID NO. 10-20; SMR3B: SEQ ID NO. 11-22; MUC7: SEQ ID NO. 11-12; ACTB: SEQ ID NO. 13-14; GAPDH: SEQ ID NO. 15-16; B2M: SEQ ID NO. 17-18; SEMG1: SEQ ID NO. 19-20; MSMB: SEQ ID NO. 21-22; PRM2: SEQ ID NO. 23-24; TGM4: SEQ ID NO. 25-26; RPS4Y1: SEQ ID NO. 27-28; SPTB: SEQ ID NO. 29-30; PPBP: SEQ ID NO. 31-32; HBB: SEQ ID NO. 11-22; FDCSP: SEQ ID NO. 11 NO.33-34; ALAS2: SEQ ID NO.35-36; HBA1: SEQ ID NO.37-38; HBD: SEQ ID NO.39-40; STC1: SEQ ID NO.41-42; MMP3: SEQ ID NO.43-44; MMP7: SEQ ID NO.45-46; PAEP: SEQ ID NO.47-48; MMP10: SEQ ID NO.49-50; XIST: SEQ ID NO.51-52; MUC4: SEQ ID NO.53-54; DKK4: SEQ ID NO.55-56;

[0060] All primers were grouped according to the expected fragment length and assigned to five different fluorescent dyes: 6-FAM, HEX, TAMRA, ROX, and L592. The first group of mRNA primers (SEQ ID NO.1 to SEQ ID NO.18) was labeled with 6-FAM fluorescent dye; the second group (SEQ ID NO.19 to SEQ ID NO.28) was labeled with HEX fluorescent dye; the third group (SEQ ID NO.29 to SEQ ID NO.40) was labeled with TAMRA fluorescent dye; the fourth group (SEQ ID NO.41 to SEQ ID NO.52) was labeled with ROX fluorescent dye; and the fifth group (SEQ ID NO.53 to SEQ ID NO.62) was labeled with L592 fluorescent dye. The arrangement of the 31 mRNA labels is shown in the diagram below. Figure 1 As shown.

[0061] (3) Determination of mRNA marker primer concentration

[0062] The amplification product fragment size and primer amplification efficiency of each mRNA marker vary significantly. Based on the amplification detection results of single genes, a multiplex amplification strategy was used to systematically validate and screen primer pairs for all mRNA markers. This process involved the detection and analysis of various bodily fluid samples, aiming to gradually adjust the concentration of primers for each gene through a series of experimental optimizations to achieve the best state of amplification balance, and ultimately determine the optimal concentration of each primer pair in the multiplex amplification detection system.

[0063] The primer sequences and final concentrations of the 31 mRNA markers are shown in Table 1.

[0064] Table 1. Primer information for 31 mRNA markers

[0065]

[0066]

[0067]

[0068] Example 2: Construction of a multiplex amplification system and detection of amplification products

[0069] Based on the successful construction of a single mRNA marker amplification system, new mRNA marker primers were gradually introduced into the system for testing. Through a series of repeated experiments, key parameters in the multiplex amplification system were adjusted, including the number of PCR cycles, annealing temperature, final extension time, amount of polymerase, total volume of the multiplex amplification reaction system, and amount of cDNA, to ensure that the multiplex amplification system could achieve stable and balanced mRNA genotyping results. After comprehensive optimization, the optimal total volume of the multiplex amplification reaction was finally determined to be 10 μL. The specific components of this system and their corresponding sample volumes are detailed in Table 2.

[0070] Table 2. Multiplex PCR amplification reaction system

[0071]

[0072] According to the manufacturer's instructions, total RNA was extracted from 200 μL of saliva, peripheral blood, semen, one oral swab, menstrual blood, and one vaginal swab using RNAiso Plus (TaKaRa, Tokyo, Japan). cDNA was then synthesized using FastKing-DNADispelling RT SuperMix (TIANGEN Biotech, Beijing, China). Reverse transcription conditions were as follows: samples were incubated at 42°C for 15 min, and the reaction was terminated by heating at 95°C for 3 min. The final cDNA volume was 20 μL, including 4 μL of 5×FastKing-RT SuperMix, 5-60 ng of RNA input, and RNase-free ddH2O to bring the volume to 20 μL.

[0073] The specific multiplex amplification procedure is shown in Table 3.

[0074] Table 3. Procedure for Multiplex PCR Amplification

[0075]

[0076] PCR amplification products were separated and detected by capillary electrophoresis using an ABI 3500 genetic analyzer (Applied Biosystems, USA). A 36cm array was used at 1.2kV with an injection time of 13s. 1 μL of amplification product was mixed with 10 μL of deionized formamide and 0.3 μL of AGCU Marker SIZ-600standard (AGCU ScienTech, Jiangsu, China). The mixture was denatured at 94℃ for 3 min, followed by an ice bath for 3 min, and then transferred to the genetic analyzer for electrophoresis. Genotyping was performed using GeneMapperID-X software v1.5 (Applied Biosystems, USA). A Bin file was created for each identified mRNA marker. All relevant Bin files were combined into a single Panel file. The Panel file was imported into the analysis method for automatic identification and interpretation of mRNA markers in the electrophoresis results.

[0077] The detection effectiveness of the 31 mRNA marker body fluid tracing system in actual human body fluid samples was further tested. Figures 2-6 The mRNA typing profiles of five different types of bodily fluid samples (peripheral blood, saliva, semen, menstrual blood, and vaginal secretions) are presented. Figure 2 As shown, all the body fluid-specific mRNA markers in the 31 mRNA marker body fluid tracing system were detectable in the corresponding body fluid samples, and the peak heights of each marker were well-balanced. This indicates that the system can effectively extract and amplify target mRNAs from different types of body fluid samples, thus achieving effective tracing analysis of body fluid samples.

[0078] Example 3: Performance evaluation and model construction of body fluid traceability using 31 mRNA markers

[0079] 5 ng RNA was extracted from 24 peripheral blood samples and 27 semen samples, and 30 ng RNA was extracted from 13 oral swabs, 15 pure saliva samples, 26 menstrual blood samples, and 22 vaginal secretion samples. Detection was performed using the multiplex amplification system, method, and amplification procedure described in Example 2. The detection rates of each marker in different body fluids were summarized (Table 4), and the body fluid identification efficacy of 31 mRNA markers was evaluated based on dimensionality reduction algorithms (PCA, t-SNE). As shown in Table 4 (where the denominator represents the number of samples tested, and the numerator represents the number of samples positive for a specific marker), for saliva-specific markers, PRH2, STATH, HTN3, FDCSP, SMR3B, and MUC7 were detected only in saliva and oral swabs. Semen-specific markers were all observed in semen samples. All six peripheral blood markers were detected in peripheral blood and menstrual blood, with SPTB showing a detection rate of only 15% (4 / 26) in menstrual blood. Menstrual blood-specific markers were detected only in menstrual blood samples. Vaginal secretion-specific markers were detectable in both vaginal secretions and menstrual blood samples, but the detection rate in menstrual blood samples was as low as 4% (1 / 26). Four vaginal secretion-specific markers, MUC4, DKK4, SFTA2, and FUT6, were detectable in oral swabs. PCA and t-SNE plots were constructed using logarithmic RFU values ​​based on 31 mRNA markers. PCA (…) of 127 samples… Figure 7 In the t-SNE plot (A), saliva, peripheral blood, semen, and vaginal secretions were separated into independent groups, while saliva and oral swabs clustered together, and menstrual blood and peripheral blood partially overlapped. Figure 7 As shown in Figure B), the 127 samples clustered into five clusters, indicating that the relative expression levels of the various humor-specific mRNA markers of these 31 mRNA markers were generally humor-specific.

[0080] Table 4. Detection rate of each mRNA marker in different body fluids

[0081]

[0082]

[0083] Based on the 31 mRNA systems and 127 sample datasets described in this invention, four different machine learning models—support vector machine, random forest, CatBoost, and neural network—were constructed for body fluid source tracing analysis. The 10-fold cross-validation confusion matrix results of these models show that the 31 specific mRNA marker combinations in this invention achieve 100% accuracy in distinguishing five body fluids (peripheral blood, semen, saliva, menstrual blood, and vaginal secretions). The accuracy, precision, recall, and F1 score of the four classifiers were calculated, and the results show that all models achieved a score of 1.0 on these metrics. The system and method described in this invention can effectively trace body fluid sources and accurately distinguish between different body fluid types such as peripheral blood, semen, saliva, menstrual blood, and vaginal secretions.

[0084] Example 4: Sensitivity and Species Specificity Detection of Multiplex Amplification System

[0085] Sensitivity tests were performed on 31 mRNA marker multiplex amplification systems. Different volumes of RNA template (30 ng, 10 ng, 5 ng, 1 ng, 0.1 ng, 0.05 ng) were added during reverse transcription according to the pre-defined RNA template amount gradient. Each gradient was tested three times to determine the sensitivity of the system. The detection rates and average peak heights for each template amount are shown below. Figure 8 As shown, the average peak height of the 31 mRNA markers decreased with decreasing RNA template amount. When the RNA template amount was greater than 1 ng, all fluid-specific mRNAs were detectable.

[0086] Species-specific detection was performed on 31 mRNA marker multiplex amplification systems. Using the multiplex amplification system, method, and amplification procedure described in Example 2, blood samples from ducks, rats, mice, pigeons, rabbits, chickens, fish, and bullfrogs were tested, with one sample collected from each animal. Each sample was tested three times to obtain species-specific results. The results showed that no specific detection products were obtained in any of the eight animal species, and no genotyping peaks were detected. This indicates that the multiplex amplification system in this embodiment of the invention has good species specificity, and non-human DNA does not interfere with the genotyping of human DNA.

[0087] Example 5: Detection of mixed samples based on a multiplex amplification system

[0088] Forty-six mixed samples were analyzed using the multiplex amplification system, method, and procedure described in Example 2. These mixed samples were composed of two different bodily fluids. The mixed samples were analyzed and predicted using the aforementioned support vector machine, random forest, CatBoost, and neural network models.

[0089] The evaluation results of the four machine learning prediction models on the mixed body fluid samples are shown in Table 5. The results were measured by accuracy, MCC, Kappa, and F1 score. The results showed that the support vector machine, CatBoost, and neural network models performed well, with accuracy, Matthews Correlation Coefficient (MCC), and Kappa value all reaching 1.0.

[0090] Table 5. Evaluation results of four machine learning prediction models on mixed body fluid samples

[0091]

[0092] Note: PB represents peripheral blood; MB represents menstrual blood; SA represents saliva; SE represents semen; VA represents vaginal secretions; AB represents a sample mixed with oral swabs and peripheral blood; AM represents a sample mixed with oral swabs and menstrual blood; AV represents a sample mixed with oral swabs and vaginal secretions; EB represents a sample mixed with semen and peripheral blood; EA represents a sample mixed with semen and oral swabs; EM represents a sample mixed with semen and menstrual blood; EV represents a sample mixed with semen and vaginal secretions.

[0093] Based on support vector machine ( Figure 9 (A), Neural Network ( Figure 9 (B), CatBoost Figure 9 The confusion matrix obtained from the C) classifier is plotted, showing that the classifier successfully predicted all 12 types of the test set. Meanwhile, the random forest ( Figure 9 The accuracy of the D) classifier was 0.98. The confusion matrix obtained based on the random forest model shows that the model mispredicted a mixture of semen and menstrual blood as menstrual blood.

[0094] Example 6: Detection of old samples based on a multiplex amplification system

[0095] Using the multiplex amplification system, method, and amplification procedure described in Example 2, 20 aged samples (stored indoors for 1-2 years) were analyzed, and a support vector machine (SVM) model was used for prediction. The SVM prediction model outputs the body fluid type of each sample with probability (Table 6). The SVM model successfully predicted the body fluid identification of the 20 samples.

[0096] Table 6. Predicted classifications and probabilities of the corresponding classifications for 20 old samples based on the support vector machine model.

[0097]

[0098]

[0099] Note: PB represents peripheral blood; MB represents menstrual blood; SA represents saliva; SE represents semen; VA represents vaginal secretions; AB represents a sample mixed with oral swabs and peripheral blood; AM represents a sample mixed with oral swabs and menstrual blood; AV represents a sample mixed with oral swabs and vaginal secretions; EB represents a sample mixed with semen and peripheral blood; EA represents a sample mixed with semen and oral swabs; EM represents a sample mixed with semen and menstrual blood; EV represents a sample mixed with semen and vaginal secretions.

[0100] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. Applications of reagents for detecting mRNA markers in any of the following aspects: (1) Tracing the origin of bodily fluids; (2) Prepare products for body fluid traceability; (3) Identification of body fluid samples; (4) Prepare products for the identification of body fluid samples; The bodily fluid samples included peripheral blood, saliva, semen, menstrual blood, and vaginal secretions. The mRNA marker consists of the following mRNA markers: Peripheral blood-specific mRNA markers: SPTB, PPBP, ALAS2, HBD, HBB, and HBA1; Semen-specific mRNA markers: SEMG1, MSMB, PRM2, and TGM4; Saliva-specific mRNA markers: STATH, HTN3, PRH2, FDCSP, MUC7, and SMR3B; Menstrual blood-specific mRNA markers: STC1, MMP3, MMP7, PAEP, and MMP10; Vaginal secretion-specific mRNA markers: MUC4, DKK4, FUT6, SFTA2, and CYP2B7P1; Male-specific mRNA marker: RPS4Y1; Female-specific mRNA marker: XIST.

2. A kit comprising a reagent for detecting the mRNA marker of claim 1, wherein the reagent comprises a detection reagent for detecting the mRNA marker; The detection reagent for detecting the mRNA marker includes primers for amplifying the mRNA marker; The detection reagent for detecting the mRNA marker also includes primers for amplifying the housekeeping gene; The housekeeping genes are B2M, ACTB, and GAPDH; The detection reagent for detecting the mRNA marker includes the nucleotide sequence shown in SEQ ID NO: 1-62.

3. The reagent kit according to claim 2, characterized in that, Primers for amplifying peripheral blood-specific mRNA markers, primers for amplifying semen-specific mRNA markers, primers for amplifying saliva-specific mRNA markers, primers for amplifying menstrual blood-specific mRNA markers, and primers for amplifying vaginal secretion-specific mRNA markers are all labeled with fluorescent dye AE, and the fluorescent dyes AE are all different. Primers for amplifying male-specific mRNA markers and primers for amplifying female-specific mRNA markers are each labeled with any one of the fluorescent dyes AE. The fluorescent dyes used to label primers for amplifying male-specific mRNA markers are different from those used to label primers for amplifying female-specific mRNA markers. Primers for amplifying housekeeping genes are labeled with any one of the fluorescent dyes AE.

4. The reagent kit according to claim 3, characterized in that, The fluorescent dye AE is selected from any one of 6-FAM, HEX, TET, ROX, TAMRA, JOE, Cy3, Cy5, and L592.

5. A method for detecting the mRNA marker described in claim 1 for tracing the origin of the mRNA in bodily fluids, comprising performing multiplex PCR using the kit described in any one of claims 2-4; The bodily fluids referred to are peripheral blood, saliva, semen, menstrual blood, and vaginal secretions.

6. The method according to claim 5, characterized in that, The method also includes detecting the composite PCR amplification products using capillary electrophoresis.

7. A method for constructing a body fluid detection prediction model, comprising obtaining the genotyping values ​​of mRNA markers in different body fluid samples, using the sample genotyping data for machine learning training, and obtaining a body fluid detection prediction model; The mRNA marker is the mRNA marker described in claim 1; The bodily fluid samples included peripheral blood, saliva, semen, menstrual blood, and vaginal secretions.

8. The construction method according to claim 7, characterized in that, The prediction model is selected from any one of the following: random forest, neural network, support vector machine, and CatBoost prediction model.

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