A two-step SERS detection method for determining the infectivity of the new coronavirus in the environment

Abstract

The present invention relates to a two-step SERS detection method for determining the infectivity of the new coronavirus in the environment. The method comprises selecting a SERS-enhanced substrate, performing SERS detection on the SARS-CoV-2 S protein and RNA, and constructing a discrimination criterion based on the differences in their SERS signals. The method then combines a machine learning method to classify the Raman spectra obtained from the two SERS detections of the new coronavirus samples present in the environment to determine the infectivity of the new coronavirus in the environment.

Description

[0001] The present invention relates to a two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment, and specifically to an identification standard constructed based on the SERS signal differences between the SARS-CoV-2 S protein and RNA. The two-step SERS detection method is designed to determine the infectivity of SARS-CoV-2 in various virus-infected environments such as cold chains and express delivery. The method belongs to the field of laser Raman spectroscopy detection technology and biological virus sensing. Background Art

[0002] Currently, SARS-CoV-2 virions have been detected on cold chain or express delivery items. A range of detection methods for SARS-CoV-2 have been developed both domestically and internationally, the most widely used of which is real-time fluorescence quantitative PCR. However, this technique can only determine the presence of the virus or its markers in the environment, not whether the virus is infectious. This is because PCR primarily detects nucleic acid fragments after the SARS-CoV-2 is broken down. However, most infected cold chain or express delivery items contain broken down, non-infectious SARS-CoV-2 virus particles, which likely contain RNA that is still present and can be detected by PCR. This can lead to non-infectious, broken down SARS-CoV-2 particles being misdiagnosed as infectious sources of the virus. Therefore, it is crucial to determine whether the virus detected on infected items in the environment is infectious.

[0003] Surface Enhanced Raman Scattering (SERS), a single-molecule spectroscopy detection technology, is expanding from environmental analysis, food safety, and cultural relic analysis to biosensing (virus detection) due to its high sensitivity, non-destructive nature, excellent repeatability, and stability. With the development of SERS detection technology, it has been applied to detect various biological samples, such as adenovirus, animal viruses, HIV, and influenza virus. Currently, this research team has successfully completed the detection of SARS-CoV-2 virus particles and the accurate identification of its Raman peaks based on SERS technology, and its detection sensitivity has reached the level of single virus detection (Patent No.: 202010371243.1; Nano-Micro Lett. (2021) 13:109.). Furthermore, it is expected that SERS detection technology can be used to determine whether the virus detected on virus-infected objects in the environment is contagious. Summary of the Invention

[0004] To this end, the purpose of the present invention is to design an experimental process for detecting biological samples based on SERS technology to provide a detection method that can determine (analyze, or judge) the infectivity of coronavirus samples, which is named a two-step SERS detection method.

[0005] On the one hand, the present invention provides a two-step SERS detection method for determining the infectivity of the new coronavirus in the environment. A SERS-enhanced substrate is selected, and SERS detection is performed on the SARS-CoV-2 S protein and RNA. A discrimination criterion is constructed based on the differences in their SERS signals. Then, a machine learning method is combined to classify the Raman spectra obtained from the two SERS detections of the new coronavirus samples present in the environment to determine the infectivity of the new coronavirus present in the environment.

[0006] The inventors have found through extensive previous research that compared to other detected molecules, the SARS-CoV-2 virus particle is an enveloped virus with a crown-like morphology and a diameter of about 70-150 nanometers, and has a larger mass and volume. In addition, the surface of the viral envelope contains three transmembrane proteins: S protein trimer, membrane protein (M) and envelope protein (E), and the envelope contains a large RNA genome. The spike glycoprotein covering the surface of the virus, which is several nanometers in size, is a key target for the development of vaccines and therapeutic antibodies and clinical diagnosis. Since the localized plasma enhancement region of the noble metal substrate is usually a region about 10 nm away from the substrate surface, and the chemical enhancement site of the semiconductor substrate is usually a molecule in contact with the substrate surface, during SERS detection, the spike S protein (nanometer-scale in length) on the surface of the new coronavirus with a complete structure will occupy the enhanced region of the noble metal or semiconductor, resulting in the SERS detection signal of the new coronavirus with a complete structure being mainly manifested as the characteristic Raman peak of the S protein, which is also the dominant Raman spectrum of the new coronavirus. However, when the new coronavirus is inactivated and the cell membrane ruptures, the RNA nucleic acid originally wrapped inside will be exposed or released to the outside of the virus, entering the SERS enhanced area and being detected by SERS.

[0007] Furthermore, the inventors creatively determined the infectivity of SARS-CoV-2 virus samples in the environment by studying the differences in the detected SERS signals of the live novel coronavirus with intact viral structure and the cleaved dead novel coronavirus.

[0008] In the present invention, the infectious virus samples to be judged in the general natural environment or body fluid environment mainly exist in the following three situations: (1) all live coronaviruses with complete virus structure; (2) all lysed dead coronaviruses; (3) a mixture of live coronaviruses with some complete virus structure and some lysed dead coronaviruses. In the present invention, the two-step SERS detection method for judging the infectiousness of coronaviruses requires the combination of machine learning to classify and distinguish the Raman spectral data obtained by the detection.

[0009] Preferably, a SERS-enhanced substrate is selected to perform the first SERS detection on multiple novel coronavirus samples in the environment, and the Raman spectra obtained from the first SERS detection are classified based on the discrimination criteria of SARS-CoV-2 S protein and RNA, combined with machine learning methods:

[0010] 1) When the Raman spectrum of the first SERS detection of the new coronavirus sample only shows the Raman peak of the S protein, the new coronavirus sample is judged to be a live coronavirus sample with a complete viral structure, that is, it is virally contagious;

[0011] 2) When the Raman spectrum of the first SERS detection of the SARS-CoV-2 sample shows Raman peaks of both the SARS-CoV-2 S protein and RNA, the SARS-CoV-2 sample is judged to be a cleaved dead coronavirus sample, or a mixed virus sample of partially intact live coronavirus and partially cleaved dead coronavirus; at this time, the infectivity of the virus sample cannot be determined.

[0012] Preferably, through the first step of SERS detection, virus samples of live coronaviruses with complete viral structures can be distinguished. Such virus samples have an extremely high risk of viral infectivity.

[0013] Preferably, after the first SERS test, virus samples whose Raman spectra show both the SARS-CoV-2 S protein and RNA Raman peaks need to undergo RNA removal and re-lysis treatment before a second SERS test. The Raman spectra obtained by the second SERS test are classified using a machine learning method:

[0014] 1) When the Raman spectrum of the second SERS test of the SARS-CoV-2 sample shows Raman peaks of both the SARS-CoV-2 S protein and RNA, the SARS-CoV-2 sample is judged to be a mixed virus sample with partially intact live coronavirus and partially broken dead coronavirus, that is, it is contagious;

[0015] 2) When the Raman spectrum of the second SERS test of the new coronavirus sample only shows the Raman peak of the S protein, the new coronavirus sample is judged to be a dead coronavirus sample that has been cleaved, that is, it is not infectious.

[0016] Ideally, the second-step SERS test can distinguish between virus samples consisting entirely of dead, fragmented coronaviruses and mixed virus samples consisting of partially intact live coronaviruses and partially fragmented, dead coronaviruses. The latter are infectious, while the former are not, which is why PCR testing techniques mistakenly diagnose them as the source of the novel coronavirus.

[0017] First, the present invention constructs a signal discrimination standard based on the SERS signal difference between SARS-CoV-2S protein and RNA, and then combines the machine learning method to discriminate and classify the large amount of Raman spectrum data obtained by the two SERS tests according to the signal discrimination standard. According to the discriminant analysis results of the Raman spectrum obtained by the first SERS test, it is found that if the Raman spectrum of the virus sample all belongs to the Raman peak of the coronavirus, then the virus sample is a live coronavirus with a complete viral structure, which has an extremely high risk of viral infection. If there is a characteristic Raman peak of viral RNA in the Raman spectrum of the virus sample, it can only be said that the virus sample contains a dead coronavirus that has been split, and its viral infectivity cannot be further determined. The virus sample needs to be removed from the RNA and re-split for a second SERS test. After the second SERS test, if there is still a characteristic Raman peak of viral RNA in the Raman spectrum of the virus sample, the virus sample is judged to be a mixed virus of partially live coronavirus with a complete viral structure and partially split dead coronavirus, which also has a certain risk of viral infection. If the Raman spectrum of the virus sample at this time all belongs to the Raman peak of the coronavirus, the virus sample is judged to be entirely lysed coronavirus, which is not infectious. This is also the case when PCR technology often misdiagnoses it as a source of coronavirus infection. Through the above two-step SERS detection method, the inventors can accurately judge the infectiousness of the presence of three coronaviruses in the environment, solving the current difficult problem that needs to be solved urgently in the new crown epidemic.

[0018] Preferably, the coronavirus includes MERS, SARS-CoV, SARS-CoV-2 and their variants.

[0019] Preferably, the SERS enhancement substrate is a noble metal SERS substrate, a semiconductor SERS substrate, or a composite SERS substrate of noble metal and semiconductor.

[0020] The preferred ones include:

[0021] (1) After the first SERS test, the virus sample whose Raman spectrum shows both the SARS-CoV-2 S protein and RNA Raman peaks contains RNA released by the cleaved coronavirus;

[0022] (2) using a magnetic bead buffer RM (e.g., TIANSeq RNAClean Beads) to remove RNA from the lysed coronavirus mixture to obtain an RNA-removed coronavirus mixture;

[0023] (3) The coronavirus mixture from which RNA has been removed is placed in an ice bath at a temperature below 4°C and re-lysed by oscillating and ultrasonicating the cell disruptor.

[0024] Furthermore, preferably, the magnetic bead buffer RM is evenly mixed with the lysed coronavirus mixture containing RNA, the magnetic beads and RNA mixture is centrifuged to the bottom of the centrifuge tube by instantaneous centrifugation, and the supernatant is taken after standing on a magnetic stand for 2 to 5 minutes to obtain the coronavirus mixture from which RNA has been removed.

[0025] Furthermore, preferably, the volume of the added magnetic bead buffer RM is 2 to 2.2 times that of the lysed coronavirus mixture containing RNA.

[0026] Furthermore, preferably, the rotation speed of the instantaneous centrifugation is 10,000 to 12,000 rpm, and the time is 1 to 2 minutes.

[0027] Furthermore, preferably, the power of the ultrasonic oscillation of the cell disruptor is 90% to 100% of the rated power of 1000 W, and the time is 60 to 120 seconds.

[0028] In the present invention, the judgment criteria of the SERS signal are first constructed by principal component analysis of the differences in the Raman spectra of the coronavirus and its RNA. Then a certain amount of three virus samples representing the presence of the new coronavirus in the general natural environment or body fluid environment are adsorbed on the SERS substrate for Raman detection. After the first SERS detection, the Raman data are discriminated and classified according to the constructed SERS signal judgment criteria by discrimination methods such as support vector machines. If the Raman spectra of the virus sample all belong to the Raman peaks of the coronavirus, the virus sample is judged to be a virus sample of a live coronavirus with a complete viral structure. This type of virus sample has extremely high infectivity. If there is a characteristic Raman peak of viral RNA in the Raman spectrum of the virus sample, it can only be said that the virus sample contains a dead coronavirus that has been split. At this time, it is impossible to further determine its viral infectivity. The virus sample needs to be subjected to RNA removal and re-split treatment before a second SERS detection. After the second SERS detection, the Raman data is still discriminated and classified using discrimination methods such as support vector machines. If the Raman spectrum of the virus sample still contains characteristic Raman peaks of viral RNA, the virus sample is judged to be a mixture of live coronavirus with partially complete viral structure and partially broken dead coronavirus. This type of virus sample also has a certain degree of viral infectivity. If the Raman spectrum of the virus sample at this time all belongs to the Raman peaks of the coronavirus, the virus sample is judged to be a completely broken coronavirus. This type of virus sample is not virally infective, and this is also the case where PCR technology often misdiagnoses it as a source of coronavirus infection. Through the above two-step SERS detection method, the infectivity of the three coronaviruses in the environment can be accurately judged.

[0029] In the present invention, the spike S protein (nanometer-scale in length) on the surface of the novel coronavirus with a complete structure will occupy the enhanced area of the noble metal or semiconductor, so the SARS-CoV-2 with a complete viral structure only shows the characteristic Raman spectrum of the SARS-CoV-2S protein. When the novel coronavirus is cleaved and inactivated, the RNA nucleic acid originally wrapped inside will be exposed or released to the outside of the virus and enter the SERS enhanced area. Therefore, the cleaved SARS-CoV-2 will simultaneously show the characteristic Raman peaks of the SARS-CoV-2S protein and RNA. The infectivity of SARS-CoV-2 in the environment can be identified by analyzing the difference in SERS signals between the coronavirus with a complete viral structure and the cleaved coronavirus.

[0030] Preferably, a machine learning method such as a support vector machine is used, and based on the established SERS signal discrimination criteria, a certain ratio of training and test sets and the most accurate linear kernel function are selected to discriminate and classify the Raman spectral data obtained from the two SERS detections. Specifically, the method of processing the Raman spectra obtained by SERS detection based on machine learning in the present invention includes:

[0031] (1) Python and Statistical Product & Service Solutions were used to perform principal component analysis (PCA) between the dominant Raman spectra of coronaviruses in different physical forms, and based on the differences in Raman shift and Raman intensity of the Raman spectra, an identification standard that can identify the new coronavirus in different physical forms was constructed.

[0032] (2) When using the support vector machine (SVW) method to classify the Raman spectra of virus samples, based on the identification criteria obtained by principal component analysis, a certain proportion of training sets and test sets were selected, and the polynomial kernel function, RBF kernel function, and linear kernel function were selected to cross-validate the training data. Finally, the linear kernel function with the highest accuracy was selected to discriminate and classify the Raman spectrum data of coronavirus samples.

[0033] Preferably, experiments on the SARS-CoV-2 virus in the two-step SERS detection method are performed in a P2 or P3 laboratory.

[0034] Beneficial effects:

[0035] In the present invention, the two-step SERS detection method developed can be used to determine the infectivity of coronaviruses present in the environment. That is, through the first step of SERS detection, it is possible to distinguish between virus samples of live coronaviruses with complete viral structures that have an extremely high risk of infection. Through the second step of SERS detection, it is possible to distinguish between mixed virus samples of live coronaviruses with partially complete viral structures and partially lysed dead coronaviruses that have a certain risk of viral infection, and virus samples that are completely lysed dead coronaviruses that are not virally infectious. This is also the case when PCR detection technology misdiagnoses them as sources of infection of the new coronavirus. This has opened up a new way to determine the infectivity of viruses on objects infected with SARS-CoV-2 in actual environments, solving the difficult problem that urgently needs to be solved in the current COVID-19 pandemic. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the structure of SARS-CoV-2 (a) with a complete viral structure and SARS-CoV-2 (b) after cleavage. The SARS-CoV-2 virus particle is an enveloped virus with a crown-like morphology. The surface of the SARS-CoV-2 virus with a complete viral structure is covered with spike glycoproteins several nanometers in size, which are the key site for entering the SERS enhancement region. In the cleavage of SARS-CoV-2, the RNA nucleic acid originally wrapped inside is exposed or released to the outside of the virus and enters the SERS enhancement region;

[0037] Figure 2 The results of principal component analysis of the Raman spectra of SARS-CoV-2 S protein and RNA show that the Raman spectra of SARS-CoV-2 S protein and RNA can be completely distinguished. Therefore, the Raman spectra of SARS-CoV-2 RNA and S protein can be used as the two main components of the SERS signal identification standard for unknown virus samples;

[0038] Figure 3 The results of principal component analysis of the Raman spectra of SARS-CoV-2 virus and SARS-CoV-2 RNA show that the Raman spectra of SARS-CoV-2 virus and SARS-CoV-2 RNA can be completely distinguished. Therefore, the Raman spectra of SARS-CoV-2 virus and SARS-CoV-2 RNA can also be used as the two main components of the SERS signal identification standard for unknown virus samples;

[0039] Figure 4 The three conditions representing the presence of novel coronavirus in the environment are: (1) all live coronaviruses with complete virus structure; (2) all cleaved dead coronaviruses; (3) a mixture of live coronaviruses with complete virus structure and cleaved dead coronaviruses;

[0040] Figure 5 This is the process for judging the infectivity of the live coronavirus samples of type (1) with complete viral structures in Example 1. Through the first SERS detection, all 82 Raman spectra of the virus sample are classified as SARS-CoV-2 S protein, indicating that the virus sample is a SARS-CoV-2 virus with a complete viral structure and has an extremely high risk of viral infection;

[0041] Figure 6 This is the process for judging the infectivity of the (2) type of split dead coronavirus sample in Example 2. Through the first SERS detection, 26 Raman spectra in the virus sample were classified as SARS-CoV-2S protein, and 60 Raman spectra were classified as SARS-CoV-2RNA, indicating that there were split SARS-CoV-2 virus particles in the virus sample, but its viral infectivity could not be judged. It is necessary to remove RNA and re-split the virus, and perform a second SERS test. At this time, all 127 Raman spectra of the virus sample were classified as SARS-CoV-2S protein, indicating that the virus sample was a split SARS-CoV-2 virus and did not have the risk of viral infection;

[0042] Figure 7 This is the process for judging the infectivity of the mixed virus sample of (3) type live coronavirus with complete virus structure and split dead coronavirus in Example 3. Through the first SERS detection, 71 Raman spectra in the virus sample were classified as SARS-CoV-2S protein, and 58 Raman spectra were classified as SARS-CoV-2RNA, indicating that there were split SARS-CoV-2 virus particles in the virus sample, but its virus infectivity could not be judged. It is necessary to remove RNA and re-split the virus, and perform a second SERS detection. At this time, 98 Raman spectra in the virus sample were classified as SARS-CoV-2S protein, and 57 Raman spectra were classified as SARS-CoV-2RNA, indicating that the virus sample was a mixed virus sample of live and dead coronaviruses, which has a certain risk of virus infection; DETAILED DESCRIPTION

[0043] The present invention is further described below through the following embodiments. It should be understood that the following embodiments are only used to illustrate the present invention, rather than to limit the present invention.

[0044] At present, the COVID-19 pandemic is facing the dilemma of globalization and temporal continuity. In order to reduce the social panic and cost consumption caused by misdiagnosis of viral infectiousness, it is very meaningful to determine whether the virus present on virus-infected objects in the environment is infectious. This field urgently needs to develop a detection method that can determine the infectiousness of SARS-CoV-2 virus samples. The signal recognition standard is constructed by the difference in SERS signals of SARS-CoV-2S protein and RNA, and then the virus sample is subjected to two SERS tests. The measured Raman data is discriminated and classified by machine learning, so as to determine the life and death and infectiousness of the new coronavirus in the SARS-CoV-2 virus sample. Of course, this method is also applicable to any other coronavirus samples, including variant viruses of MERS, SARS-CoV, and SARS-CoV-2.

[0045] Specifically, the innovation of the present invention lies in that a two-step SERS detection method is proposed to determine the infectivity of the coronavirus present in the environment. Since SARS-CoV-2 with a complete viral structure only shows the characteristic Raman peak of the SARS-CoV-2S protein, and the cleaved SARS-CoV-2 will simultaneously show the characteristic Raman peaks of the SARS-CoV-2S protein and RNA, the judgment criteria of the SERS signal are first constructed based on the difference in the Raman signals of the SARS-CoV-2S protein and RNA. Based on the judgment criteria of the SERS signal, the first step of SERS detection is used to distinguish the highly contagious live coronavirus virus samples with a complete viral structure. The second step of SERS detection is then used to distinguish the mixed virus samples of live and dead coronaviruses with a certain viral infectivity and the virus samples of dead coronaviruses that are not virally infective.

[0046] In the present invention, the signal enhancement substrate used in the two-step SERS detection method can be various types of noble metal substrates, various types of semiconductor substrates, and various types of noble metal and semiconductor composite substrates, which can significantly enhance the Raman signal of the detected coronavirus sample.

[0047] In the present invention, the viral load range of the viral samples to be judged as infectious is wide, and for most SERS substrates with single-molecule detection capabilities, the detection of viral samples can also reach the single-virus detection level.

[0048] The following uses a semiconductor powder SERS substrate as an example to illustrate the complete process of the two-step SERS detection method for determining the infectivity of coronaviruses in the environment.

[0049] SERS detection of SARS-CoV-2 S protein and RNA: 50–1000 μL of a specific viral load of SARS-CoV-2 S protein and RNA was ultrasonically mixed with 0.01–0.05 g of a semiconductor powder SERS substrate and adsorbed for 20–30 minutes. The mixture was then centrifuged at 15,000–16,000 rpm for 5–10 minutes to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5–10 μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35–45°C, SERS detection was performed, yielding a large amount of Raman spectral data for the SARS-CoV-2 S protein and RNA.

[0050] Constructing discrimination criteria based on SARS-CoV-2 S protein and RNA Raman signals: Using Python and Statistical Product & Service Solutions, principal component analysis was performed on the Raman signals of SARS-CoV-2 S protein and RNA obtained by SERS detection. The difference in Raman peaks of SARS-CoV-2 S protein and SARS-CoV-2 RNA was obtained, that is, the peak of SARS-CoV-2 S protein at about 752 cm -1 、1380cm -1 and 1520cm -1 Three characteristic Raman peaks were observed at the sites. A discrimination standard for SARS-CoV-2 RNA and SARS-CoV-2 S protein was constructed based on this, which will be used to determine the infectiousness of the new coronavirus in the environment.

[0051] First SERS test: Virus samples with a certain viral load and representing three conditions of the presence of the new coronavirus in the environment were placed in three clean centrifuge tubes, namely: (1) 50-1000 μL of live coronavirus with complete viral structure; (2) 100-2000 μL of dead coronavirus with complete viral structure; (3) 100-2000 μL of a mixture of live coronavirus with complete viral structure and dead coronavirus with complete viral structure. The three virus sample solutions were then ultrasonically mixed with 0.01-0.05g of semiconductor powder SERS substrate and adsorbed for 20-30 minutes. The mixture of virus sample and semiconductor powder was then centrifuged at 15000-16000 r / min for 5-10 minutes to pool at the bottom of the centrifuge tube. The supernatant was removed and only the last 5-10 μL of the mixed liquid was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, the SERS test was performed. During the detection, multiple detection areas on the surface of the substrate containing the above three virus samples were randomly selected for Raman detection to obtain a large amount of usable SERS spectral data.

[0052] Preferably, there are two ways to adsorb the virus sample solution onto the metal or semiconductor with SERS activity: immersion and dripping. When using the immersion method for adsorption, the metal or semiconductor substrate is generally powder, and the ratio of substrate powder to virus sample solution is 0.01-0.05g:50-1000μL. When using the dripping method for adsorption, the metal or semiconductor substrate is generally a chip with an area of 0.5cm×0.5cm, and the volume of the virus sample solution dripped in is 10-50μL. Preferably, the virus sample solution is immersed in the metal or semiconductor SERS substrate powder for about 20-30min. Preferably, the volume of the mixed solution after centrifugation and concentration dropped on the glass substrate for SERS detection is about 5-10μL, and the centrifugal speed and time are about 15000-16000r / min, and the time is 5-10min.

[0053] First discriminant analysis of Raman data: Based on the constructed discrimination criteria for SARS-CoV-2 RNA and SARS-CoV-2 S protein, the first Raman detection spectral data of the three virus samples were discriminated and classified using the support vector machine discrimination method. According to the discrimination results of the Raman data obtained in the first detection, if all the Raman spectra of the virus sample are judged to belong to the SARS-CoV-2 S protein, then the virus sample is determined to be (1) a SARS-CoV-2 virus with a complete viral structure, which has a serious risk of viral infection. If the virus sample has a Raman spectrum that is judged to belong to SARS-CoV-2 RNA, it can only mean that the virus sample contains a split SARS-CoV-2 virus, and its viral infectivity cannot be accurately determined. In summary, after the first Raman detection and discriminant analysis, the infectivity of (1) live coronavirus samples with complete viral structures can be accurately identified, while the infectivity of (2) completely lysed dead coronavirus samples and (3) mixed virus samples of dead and live coronaviruses cannot be determined. Therefore, the viral samples need to be RNA removed and re-lysed for a second Raman detection.

[0054] RNA removal and re-lysis: TIANSeq RNA Clean Beads were used to remove RNA from the lysed coronavirus mixture. First, the magnetic bead buffer RM that had been equilibrated to room temperature was shaken and mixed evenly, and then about 2 to 2.2 times the volume of the coronavirus mixture was added to 50 to 1000 μL of (2) and (3) coronavirus samples and shaken to mix evenly. Then, the mixed liquid of the two types of virus samples and the magnetic bead buffer was centrifuged at 10000 to 12000 r / min for 1 to 2 minutes to the bottom of the centrifuge tube, and placed on a magnetic stand for 2 to 5 minutes. After the viral RNA was completely adsorbed by the magnetic beads, the upper liquid was transferred to a clean standby centrifuge tube. Finally, the 50 to 1000 μL upper supernatant obtained above from which the viral RNA had been removed was placed in an ice bath controlled at 0 to 4°C and shaken for 60 to 120 seconds by a cell disruptor for re-lysis. At this time, there is no RNA in the (2) type of dead coronavirus sample solution that is completely lysed, while there is still re-lysed RNA in the (3) type of mixed virus sample solution of dead and live coronaviruses.

[0055] Preferably, the solution used to dilute the virus sample is denuclearized water. Preferably, the volume of the mixed virus sample solution placed in the cell disruptor for lysis is about 600 to 2000 μL.

[0056] Second SERS detection: The 50-1000 μL of the above-obtained mixed virus sample solution of (2) type completely lysed dead coronavirus sample and (3) type dead and live coronavirus were ultrasonically mixed with 0.01-0.05g semiconductor powder SERS substrate and adsorbed for 20-30 minutes. Then, the mixture of virus sample and semiconductor powder was centrifuged at a speed of 15000-16000 r / min for 5-10 minutes to collect at the bottom of the centrifuge tube. The supernatant was removed and only the last 5-10 μL of the mixed liquid was dripped onto the cleaned glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed. During the detection, multiple detection areas on the surface of the substrate containing the above three virus samples were randomly selected for Raman detection to obtain a large amount of usable SERS spectral data.

[0057] Second discriminant analysis of Raman data: Based on the constructed discrimination criteria for SARS-CoV-2RNA and SARS-CoV-2S protein, the support vector machine discrimination method is used to discriminate and classify the second Raman detection spectrum data of the obtained (2) and (3) coronavirus samples. According to the discrimination results of the Raman data obtained by the second detection, if there are still Raman spectra in the virus sample that are discriminated as belonging to SARS-CoV-2RNA, then the virus sample is determined to be (3) dead or alive coronavirus, which has a certain risk of virus transmission. If all Raman spectra of the virus sample at this time are judged to belong to SARS-CoV-2S protein, then the virus sample is determined to be (2) dead coronavirus that is completely cleaved. This type of virus sample is not virally contagious, and this is also the case where it is misdiagnosed as the source of infection of the new coronavirus by PCR detection technology.

[0058] In the present invention, a discrimination criterion based on the SERS signal difference of SARS-CoV-2 S protein and RNA is first constructed, and then a two-step SERS detection method is designed to accurately judge the viral infectivity of SARS-CoV-2 in various virus-infected environments such as cold chain and express delivery, as well as to judge whether there are pathogenic split viruses in human body fluids (for example, false positives in a short period of time after vaccination in the human environment and other situations). The false positives in a short period of time after vaccination are the viral infectivity of the following three types of new coronaviruses: (1) live SARS-CoV-2 virus samples with extremely high viral infectivity, (2) dead SARS-CoV-2 virus samples with no viral infectivity, and (3) mixed virus samples of partially intact live new coronaviruses and partially split dead new coronaviruses with certain viral infectivity. This solves the problem of determining viral infectivity that cannot be solved by current PCR technology, thereby reducing unnecessary social panic and social cost consumption caused by misdiagnosis of new coronavirus infectivity, which is of great significance for the effective control of the new coronavirus epidemic.

[0059] The following further examples include ultrasensitive semiconductor SERS substrates, 1.33×10 -6 mol / L SARS-CoV-2S protein, 10 7 copies / mL SARS-CoV-2 RNA and 10 7copies / mL SARS-CoV-2 examples are provided to illustrate the present invention in detail. It should also be understood that the following examples are only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Some non-essential improvements and adjustments made by those skilled in the art based on the above content of the present invention belong to the scope of protection of the present invention. The specific process parameters and the like in the following examples are only an example within a suitable range, that is, those skilled in the art can make a selection within a suitable range based on the description herein, and are not limited to the specific values ​​exemplified below. In the following examples and comparative examples, unless otherwise specified, the centrifugal speed used is 15000r / min and the time is 10min, and all experiments on SARS-CoV-2 are carried out in P2 or P3 laboratories. The magnetic bead buffer RM used is TIANSeq RNAClean Beads.

[0060] Example 1

[0061] The discrimination criteria were constructed based on the Raman signals of SARS-CoV-2 S protein and RNA: 500 μL of 1.33×10 -6 mol / L SARS-CoV-2 S protein and 10 7 10 copies / mL of SARS-CoV-2 RNA was ultrasonically mixed with 0.02g of an ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of viral S protein, RNA, and ultrasensitive SERS semiconductor powder was then centrifuged to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed, generating a large amount of Raman spectral data for SARS-CoV-2 S protein and RNA. Principal component analysis of the Raman signals of SARS-CoV-2 S protein and RNA obtained by SERS detection was then performed using Python and Statistical Product & Service Solutions. The Raman peak differences between SARS-CoV-2 S protein and SARS-CoV-2 RNA were identified, and a discriminatory criterion for SARS-CoV-2 RNA and SARS-CoV-2 S protein was constructed based on this.

[0062] The first SERS detection and the discrimination analysis of Raman signal: (1) 200μL of 10 7A sample of live coronavirus with a total of 100 copies / mL and a complete virus structure was placed in a clean centrifuge tube and ultrasonically mixed with 0.02g of ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of virus sample and ultrasensitive SERS semiconductor was then centrifuged to the bottom of the centrifuge tube, the supernatant was removed, and only the last 5μL of the mixed liquid was dripped onto the clean glass substrate. After drying in a clean oven at 35-45°C, multiple detection areas on the surface of the virus sample were randomly selected for SERS detection to obtain a large amount of available SERS spectral data. Based on the constructed discrimination criteria for SARS-CoV-2 RNA and SARS-CoV-2 S protein, the Raman spectral data of the (1) virus sample were discriminated and classified using the support vector machine discrimination method. It was found that the Raman spectra obtained by the first SERS detection at this time were all judged to belong to SARS-CoV-2 S protein. The composition of the corresponding virus sample was all live coronavirus with a complete virus structure, which had an extremely high risk of virus transmission.

[0063] Example 2

[0064] The discrimination criteria were constructed based on the Raman signals of SARS-CoV-2 S protein and RNA: 500 μL of 1.33×10 -6 mol / L SARS-CoV-2 S protein and 10 7 10 copies / mL of SARS-CoV-2 RNA was ultrasonically mixed with 0.02g of an ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of viral S protein, RNA, and ultrasensitive semiconductor powder was then centrifuged to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed, generating a large amount of Raman spectral data for SARS-CoV-2 S protein and RNA. Principal component analysis of the Raman signals of SARS-CoV-2 S protein and RNA obtained by SERS detection was then performed using Python and Statistical Product & Service Solutions. The Raman peak differences between SARS-CoV-2 S protein and SARS-CoV-2 RNA were identified, and a discriminatory criterion for SARS-CoV-2 RNA and SARS-CoV-2 S protein was constructed based on this.

[0065] The first SERS detection and the discrimination analysis of Raman signal: (2) 400μL of 10 7A completely lysed dead coronavirus sample of 100 copies / mL was placed in a clean centrifuge tube, and 200 μL of the virus sample was separated for the first SERS detection. It was ultrasonically mixed with 0.02g of ultrasensitive semiconductor SERS substrate and adsorbed for 20 to 30 minutes. Then, the mixture of the virus sample and the ultrasensitive SERS semiconductor powder was concentrated to the bottom of the centrifuge tube by centrifugation, the supernatant was removed, and only the last 5 μL of the mixed liquid was taken and dropped on the clean glass substrate. After drying in a clean oven at 35 to 45°C, multiple detection areas on the surface of the virus sample were randomly selected for SERS detection to obtain a large amount of available SERS spectral data. Then, based on the constructed discrimination criteria for SARS-CoV-2 RNA and SARS-CoV-2 S protein, the Raman spectral data of the (2) virus sample was discriminated and classified using the support vector machine discrimination method. It was found that the Raman spectrum of SARS-CoV-2 RNA existed in the Raman spectrum obtained by the first SERS detection. This only means that there is a lysed SARS-CoV-2 virus in the (2) virus sample, and its viral infectivity cannot be accurately judged. Therefore, it is necessary to remove RNA and re-lyse the virus sample, and perform a second SERS detection and discriminant analysis of the Raman signal.

[0066] RNA removal and re-lysis: RNA was removed from the lysed coronavirus mixture using TIANSeq RNA Clean Beads. First, the magnetic bead buffer RM that had been equilibrated to room temperature was shaken and mixed evenly, and then 440 μL of magnetic bead buffer RM was added to the remaining 200 μL of the (2) coronavirus sample and shaken to mix evenly. The mixed liquid of the virus sample and magnetic bead buffer was then centrifuged at 12000 r / min for 1 minute to the bottom of the centrifuge tube, and placed on a magnetic stand for 5 minutes. After the viral RNA was completely adsorbed by the magnetic beads, the upper liquid was transferred to a clean standby centrifuge tube. Finally, the 640 μL upper supernatant obtained above, from which the viral RNA had been removed, was placed in an ice bath controlled at 0-4°C and re-lysed by shaking with a cell disruptor for 120 seconds. At this point, SARS-CoV-2 RNA no longer existed in the (2) dead coronavirus sample solution that was completely lysed.

[0067] Second SERS Detection and Raman Signal Discrimination Analysis: Approximately 640 μL of the viral sample solution, obtained after RNA removal and relysis, was ultrasonically mixed with 0.02 g of the ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of viral sample and ultrasensitive SERS semiconductor powder was then centrifuged to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5 μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed on several randomly selected detection areas on the surface containing the viral sample, generating a large amount of usable SERS spectral data. Based on the constructed discrimination criteria of SARS-CoV-2 RNA and SARS-CoV-2S protein, the support vector machine discrimination method was used to discriminate and classify the Raman spectral data of the (2) type virus samples after RNA removal and re-lysis treatment. It was found that the Raman spectra obtained by the second SERS detection at this time were all judged to belong to SARS-CoV-2S protein. The composition of the corresponding virus samples was all dead coronaviruses that were lysed, and there was no risk of viral transmission. This was also the case where the samples were misdiagnosed as the source of infection of the new coronavirus by PCR detection technology.

[0068] Example 3

[0069] The discrimination criteria were constructed based on the Raman signals of SARS-CoV-2 S protein and RNA: 500 μL of 1.33×10 -6 mol / L SARS-CoV-2 S protein and 10 7 10 copies / mL of SARS-CoV-2 RNA was ultrasonically mixed with 0.02g of an ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of viral S protein, RNA, and ultrasensitive semiconductor powder was then centrifuged to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed, generating a large amount of Raman spectral data for SARS-CoV-2 S protein and RNA. Principal component analysis of the Raman signals of SARS-CoV-2 S protein and RNA obtained by SERS detection was then performed using Python and Statistical Product & Service Solutions. The Raman peak differences between SARS-CoV-2 S protein and SARS-CoV-2 RNA were identified, and a discriminatory criterion for SARS-CoV-2 RNA and SARS-CoV-2 S protein was constructed based on this.

[0070] The first SERS detection and the discrimination analysis of Raman signal: (3) 400μL of 10 7A mixed virus sample of 200 copies / mL of live coronavirus with intact viral structure and partially cleaved dead coronavirus was placed in a clean centrifuge tube, and 200 μL of the virus sample was separated for the first SERS test. This was ultrasonically mixed with 0.02 g of ultrasensitive semiconductor SERS substrate and adsorbed for 20 to 30 minutes. The mixture of virus sample and ultrasensitive semiconductor powder was then centrifuged to the bottom of the centrifuge tube. The supernatant was removed, and only the final 5 μL of the mixed liquid was dripped onto a clean glass substrate. After drying in a clean oven at 35 to 45°C, SERS detection was performed on multiple detection areas on the surface containing the virus sample, obtaining a large amount of usable SERS spectral data. Then, based on the constructed discrimination criteria of SARS-CoV-2 RNA and SARS-CoV-2 S protein, the Raman spectrum data of the (3) type virus sample was discriminated and classified using the support vector machine discrimination method. It was found that the Raman spectrum obtained by the first SERS detection at this time contained the Raman spectrum of SARS-CoV-2 RNA, which only means that there is a cleaved SARS-CoV-2 virus in the (3) type virus sample. This is also consistent with the Raman spectrum of the (2) type virus sample, and its viral infectivity cannot be accurately judged. Therefore, it is necessary to remove the RNA and re-cleave the virus sample, and perform a second SERS detection and discrimination analysis of the Raman signal.

[0071] RNA removal and re-lysis: RNA was removed from the lysed coronavirus mixture using TIANSeq RNA Clean Beads. First, the magnetic bead buffer RM that had been equilibrated to room temperature was shaken and mixed evenly, and then 440 μL of magnetic bead buffer RM was added to the remaining 200 μL of (3) coronavirus sample and shaken to mix evenly. The mixed liquid of the virus sample and magnetic bead buffer was then centrifuged at 12000 r / min for 1 minute to the bottom of the centrifuge tube, and placed on a magnetic stand for 5 minutes. After the viral RNA was completely adsorbed by the magnetic beads, the upper liquid was transferred to a clean standby centrifuge tube. Finally, the 640 μL upper supernatant obtained above, from which the viral RNA had been removed, was placed in an ice bath controlled at 0-4°C and re-lysed by shaking with a cell disruptor for 120 seconds. At this time, the re-lysed RNA appeared in the mixed virus sample solution of (3) dead and live coronaviruses.

[0072] Second SERS Detection and Raman Signal Discrimination Analysis: Approximately 640 μL of the viral sample solution, obtained after RNA removal and relysis, was ultrasonically mixed with 0.02 g of the ultrasensitive semiconductor SERS substrate and adsorbed for 20-30 minutes. The mixture of viral sample and ultrasensitive SERS semiconductor powder was then centrifuged to pool at the bottom of the centrifuge tube. The supernatant was removed, and only the final 5 μL of the mixture was dripped onto a clean glass substrate. After drying in a clean oven at 35-45°C, SERS detection was performed on several randomly selected detection areas on the surface containing the viral sample, generating a large amount of usable SERS spectral data. Based on the constructed discrimination criteria of SARS-CoV-2 RNA and SARS-CoV-2S protein, the support vector machine discrimination method was used to discriminate and classify the Raman spectral data of the (3) type virus sample after RNA removal and re-lysis treatment. It was found that the Raman peak of SARS-CoV-2 RNA still existed in the Raman spectrum obtained by the second SERS detection. The composition of this type of virus sample is a mixture of live coronavirus with complete virus structure and cleaved dead coronavirus, which poses a certain risk of virus transmission.

[0073] Examples 1, 2, and 3 correspond to the complete process of determining the infectivity of the novel coronavirus in three environments using a two-step SERS assay. For any virus sample collected from an infected environment, based on the established criteria for distinguishing SARS-CoV-2 RNA and SARS-CoV-2 S protein, the two-step SERS assay provides a clear assessment of the viral composition and corresponding infectivity of the sample, resolving the challenge of determining viral infectivity that currently remains unresolved using PCR technology.

Claims

1. A two-step SERS detection method for determining the infectivity of the novel coronavirus in the environment. This method uses a SERS-enhancing substrate to perform SERS detection on the SARS-CoV-2 S protein and RNA, and constructs a discrimination criterion based on the differences in their SERS signals. Machine learning is then used to classify the Raman spectra obtained from the two SERS detections of the novel coronavirus sample in the environment to determine the infectivity of the novel coronavirus in the environment. It is characterized by: We selected a SERS-enhanced substrate and performed the first SERS detection on multiple COVID-19 samples in the environment. We then used machine learning methods to classify the Raman spectra obtained from the first SERS detection based on the discrimination criteria for SARS-CoV-2 S protein and RNA: 1) When the Raman spectrum of the first SERS detection of the new coronavirus sample only shows the Raman peak of the S protein, the new coronavirus sample is judged to be a live coronavirus sample with a complete viral structure, that is, it is virally contagious; 2) When the Raman spectrum of the SARS-CoV-2 sample in the first SERS test shows Raman peaks of both the SARS-CoV-2 S protein and RNA, the SARS-CoV-2 sample is judged to be a fragmented dead coronavirus sample, or a mixed virus sample of partially intact live coronavirus and partially fragmented dead coronavirus. At this point, the infectiousness of the virus sample cannot be determined; After the first SERS test, virus samples whose Raman spectra show both the SARS-CoV-2 S protein and RNA Raman peaks need to undergo RNA removal and re-lysis before a second SERS test. The Raman spectra obtained from the second SERS test are classified using machine learning methods: 1) When the Raman spectrum of the second SERS test of the new coronavirus sample only shows the Raman peak of the S protein, the new coronavirus sample is judged to be a dead coronavirus sample that has been cleaved, that is, it is not infectious; 2) When the Raman spectrum of the second SERS test of the new coronavirus sample shows Raman peaks of the new coronavirus S protein and RNA at the same time, the new coronavirus sample is judged to be a mixed virus sample of partially intact live coronavirus and partially broken dead coronavirus, that is, it is virally contagious.

2. The two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment according to claim 1, characterized in that: The coronaviruses include MERS, SARS-CoV, SARS-CoV-2 and their variants.

3. The two-step SERS detection method for determining the infectivity of the new coronavirus in an environment according to claim 1, characterized in that: The SERS enhancement substrate is a noble metal SERS substrate, a semiconductor SERS substrate, or a composite SERS substrate of noble metal and semiconductor.

4. The two-step SERS detection method for determining the infectivity of the new coronavirus in an environment according to claim 1, characterized in that: include: (1) After the first SERS test, the virus sample whose Raman spectrum shows both the SARS-CoV-2 S protein and RNA Raman peaks contains RNA released by the cleaved coronavirus; (2) using magnetic bead buffer RM to remove RNA from the lysed coronavirus mixture to obtain an RNA-removed coronavirus mixture; (3) The coronavirus mixture from which RNA has been removed is placed in an ice bath at a temperature below 4°C and re-lysed by oscillating and ultrasonicating the cell disruptor.

5. The two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment according to claim 4, characterized in that: Mix the magnetic bead buffer RM and the RNA-containing lysed coronavirus mixture evenly, centrifuge the magnetic beads and RNA mixture to the bottom of the centrifuge tube by instantaneous centrifugation, place it on a magnetic stand and let it stand for 2 to 5 minutes, then take the supernatant to obtain the RNA-removed coronavirus mixture.

6. The two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment according to claim 5, characterized in that: The volume of magnetic bead buffer RM added is 2 to 2.2 times that of the lysed coronavirus mixture containing RNA.

7. The two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment according to claim 5, characterized in that: The speed of the instantaneous centrifugation is 10,000 to 12,000 rpm, and the time is 1 to 2 minutes.

8. The two-step SERS detection method for determining the infectivity of the novel coronavirus in an environment according to claim 4, characterized in that: The power of the ultrasonic oscillation of the cell disruptor is 90% to 100% of the rated power of 1000W, and the time is 60 to 120 seconds.

9. The two-step SERS detection method for determining the infectivity of a novel coronavirus in an environment according to any one of claims 1 to 8, characterized in that: The support vector machine machine learning method was adopted, and based on the constructed SERS signal discrimination criteria, a certain proportion of training sets and test sets were selected, and the polynomial kernel function, RBF kernel function and linear kernel function were selected to cross-validate the training data. Finally, the linear kernel function with the highest accuracy was selected to discriminate and classify the Raman spectral data obtained from the two SERS detections.

10. The two-step SERS detection method for determining the infectivity of a novel coronavirus in an environment according to claim 9, characterized in that: Experiments on the SARS-CoV-2 virus in the two-step SERS detection method were all conducted in P2 or P3 laboratories.

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