Single-cell tissue in-situ sequencing technique based on raman spectroscopy sorting

Abstract

The present invention relates to the technical field of microbial single-cell sequencing and in particular to a single-cell tissue in-situ sequencing method based on Raman spectroscopy sorting technology. The method comprises: preserving the position information of microbial single cells in a tissue by means of tissue sectioning, and identifying and sorting the microbial single cells in the tissue in a section on the basis of Raman spectroscopy to obtain desired target single cells, without relying on labeling with probes and the like, thereby realizing in-situ detection of the single cells at the tissue level and obtaining a single-cell genome and single cell transcriptome information. The microbial morphology of the desired tissue section is directly observed under a microscope and Raman sorting is performed, without the need for steps such as enzyme penetration, digestion, and labeling, and thus, the operation is simple. The microbial single cells in the tissue are precisely sorted by means of the cell morphology and spectral comparison, and the species are identified by means of transcriptome analysis, so that the transcriptional change of genes at the microbial single-cell level during host-microbe interaction can be revealed.

Description

Single-cell tissue in-situ sequencing technology based on Raman spectrum sorting TECHNICAL FIELD

[0001] The present application relates to the field of single-cell sequencing technology of microorganisms, in particular to a single-cell tissue in-situ sequencing technology based on Raman spectrum sorting. BACKGROUND

[0002] Raman spectrum is a kind of scattering spectrum, which is the phenomenon that the frequency of incident light changes after being scattered due to the fact that the molecular bond in a compound is excited to a virtual state but has not yet returned to the original state. Each single-cell Raman spectrum is composed of more than 1500 Raman peaks corresponding to a type of chemical bond respectively, which can be used as a "molecular fingerprint" unique to a single cell, and further reflect the multi-dimensional information of the composition and content of chemical substances in a specific cell. The collection of single-cell Raman spectra of a cell population in a specific space-time state is called "Raman group". The Raman group can quickly and low-costly measure and monitor the "metabolome" with single-cell precision, and the changes thereof can reflect and characterize the panorama and nearly infinite "state" and "function" of the cell system. In addition, water molecules have no strong signal interference in the key fingerprint area, which makes it have the ability of in-vivo detection. Therefore, the Raman group technology has almost all the characteristics required by an ideal single-cell phenotype analysis or sorting technology, and is an ideal tool for single-cell phenome identification. The spontaneous Raman signal of most cell phenotypes in nature is weak. In order to stably improve the sensitivity of Raman detection, a surface-enhanced Raman probe technology based on labeling strategy has been developed. By detecting the strong Raman signal of the SERS probe, in-vivo tumor targeting detection, polysaccharide detection in living cells, specific detection of glycosylated proteins, telomere length evaluation, micro RNA detection, dynamic tracking of cell membrane repair, and O-GIc NAcvlation imaging research in single living cells have been achieved. Based on the powerful phenotype identification ability of single-cell Raman spectrum technology, a series of Raman activated cell sorting platforms (RACS) have been developed.

[0003] As a label-free, non-invasive, real-time and rapid phenotype detection method, Raman spectrum can provide a large amount of information such as molecular conformation, electronic distribution and mutual influence among molecular forces. The "single-cell Raman spectrum" composed of the Raman signals of all compounds in the cell can be used as its "chemical fingerprint" for the differentiation and identification of cell species, and has the advantages of rapidness, sensitivity and non-destructiveness.

[0004] Interactions and assembly among cells depend on the three-dimensional structure in which the cells are located. The spatial location of cells and the characteristics of cells themselves are crucial for determining how tissues function or fail when disease occurs; spatial omics combines the spatial location information of cells in tissues with the measurement of cell gene expression and cell gene structure, thereby interpreting cell-cell communication, tissue structure and function, and disease mechanisms. However, microbial communities have rich taxonomic diversity and spatial organization, and existing technologies are difficult to simultaneously perform high taxonomic resolution and high spatial resolution microbial community analysis (Z. Cao et al., Spatial profiling of microbial communities by sequential FISH with error-robust encoding. Nat Commun 14, 1477 (2023).). Currently, researchers have made the following attempts to study tissue-located microbial spatial omics:

[0005] (1) HiPR-FISH (high-phylogenetic-resolution microbiome mapping by fluorescence in situ hybridization): HiPR-FISH achieves spatial localization and species identification of hundreds of microbial species in complex communities through fluorescence in situ hybridization, binary encoding, spectral imaging, and machine learning decoding (H. Shi et al., Highly multiplexed spatial mapping of microbial communities. Nature 588, 676-681 (2020).). First, probes are designed according to the 16s rRNA sequence of target microorganisms, which contain 16S rRNA sequences that specifically bind to target microorganisms and fluorescent dyes (there can be multiple fluorescent groups). Through the combination of multiple dye groups, the sample can be imaged with 1023 unique combinations at a time on a standard confocal microscope, and the spectral data at different excitation wavelengths can be spliced into a spectral barcode. Therefore, this technology can identify hundreds of microorganisms at the genus level, spatially locate them at the single-cell level, and statistically analyze microbial abundance through synthetic probe FISH hybridization. SEER-FISH (sequential error-robust fluorescence in situ hybridization), which is similar in principle to HiPR-FISH, can perform multiple rounds of fluorescence imaging to identify microbial groups.

[0006] (2) MaPS-seq (metagenomic plot sampling by sequencing): Although the metagenomic analysis technology based on FISH imaging can retain the location information of microbial communities, this method has the defects of the need for pre-designed probes and low resolution. MaPS-seq, as a new technology, can analyze the spatial composition of tissue microbial communities at the micron scale. MaPS-seq fixes the metagenomic sample in a gel matrix and breaks the tissue into particles by freezing. The 16S rRNA amplification primers with barcodes are combined with the microorganisms in the particle classification unit and deep sequencing is performed. Data analysis reveals the heterogeneous distribution and positive and negative correlation between different microbial classification units (R. U. Sheth et al., Spatial metagenomic characterization of microbial biogeography in the gut. Nat Biotechnol 37, 877-883 (2019)).

[0007] (3) SHM-seq (Spatial Host-Microbiome Sequencing): This technology realizes multi-omics analysis by performing histology, spatial RNA sequencing of tissue cells, and spatial 16S rRNA sequencing of tissue microorganisms on a glass array, simultaneously capturing and analyzing host mRNA and bacterial 16S RNA sequences. The main steps of this method are as follows: First, make tissue sections; make a glass array containing specific labels for simultaneously capturing the variable regions of host mRNA and 16S rRNA; perform enzymatic digestion to make cells permeable so as to capture host mRNA and bacterial 16S RNA sequences; perform cDNA synthesis on the array surface, and perform qPCR quantification on the released cDNA material. Illumina finger index is used to build a library, sequence cDNA (B. Lotstedt, M. Strazar, R. Xavier, A. Regev, S. Vickovic, Spatial host-microbiome sequencing reveals niches in the mouse gut. Nat Biotechnol, (2023).).

[0008] Although the above methods can retain the location information of tissue microbial communities, these methods all rely on the conserved region of microbial 16S rRNA for microbial identification. Therefore, the above sequencing methods do not belong to the method of spatial single-cell sequencing of tissue microorganisms.

[0009] SUMMARY

[0010] The present application preserves the position information of the microbial single cells in the tissue through the tissue section, identifies the microbial single cells of the tissue in the section based on the Raman spectrum, sorts, and obtains the required target single cells, and does not rely on probes and the like for labeling, so as to realize in-situ detection of the single cell tissue level and obtain single cell genome and single cell transcriptome information, and on this basis, the present application is completed.

[0011] In a first aspect, the present application provides a single cell tissue in-situ sequencing analysis method based on Raman spectrum, which comprises the following steps:

[0012] S01. Tissue section sample preparation, obtaining a tissue sample to be tested, and obtaining a tissue section with a certain thickness through freezing, embedding, and sectioning;

[0013] S02. Placing the tissue section in step S01 on a Raman chip, observing the tissue sample under a Raman microscope, and collecting Raman spectra of microorganisms and tissue cells;

[0014] S03. Determining the corresponding single cells to be sorted in combination with the morphology of the tissue and microorganisms in the field of view of the Raman microscope;

[0015] S04. Positioning the laser spot to the cell to be sorted under the Raman microscope, ejecting the cell into a receptacle for standby, and completing the sorting;

[0016] S05. After transferring the single cell in the receptacle in the above S04 step, lysing the cell to obtain cell lysate, and completing single cell tissue in-situ sequencing through genome amplification, library construction, and sequencing.

[0017] Further, the sequencing in step S05 includes genome sequencing and transcriptome sequencing; when the sequenced single cell is used for biological species identification, genome sequencing and / or transcriptome sequencing are preferred; when the sequenced single cell is used to explain the interaction between the host and the microorganism, transcriptome sequencing is preferred.

[0018] Further, the thickness of the tissue section is 5-10 μm; preferably 7-8 μm.

[0019] Further, the laser wavelength in the Raman spectrum collection is 525-550 nm, preferably 532-535 nm.

[0020] Further, the Raman spectrum collection time is 2-8 s, preferably 5-6 s.

[0021] Further, the laser intensity in the laser spot positioning is 1-5 mW; preferably 3-4 mW.

[0022] Further, the laser energy is 55-70 nj; preferably 60-65 nj.

[0023] Further, the lysis time is 8-15 min, preferably 10-12 min.

[0024] Further, the sample to be tested includes sources from pathological tissues, body fluids, food, drugs, cosmetics or environment, etc.

[0025] Further, the Raman spectrum sorting is Raman ejection sorting (RACE).

[0026] In a second aspect, the present application provides a single-cell in situ sequencing device, which is composed of a tissue sample processing module, a tissue sample single-cell Raman spectrum acquisition module, a tissue sample single-cell sorting module, a tissue sample single-cell lysis module, and a single-cell in situ sequencing module, wherein:

[0027] The tissue sample processing module is to obtain a tissue sample to be tested, and to obtain a tissue slice with a certain thickness by freezing, embedding, and slicing processing.

[0028] The tissue sample single-cell Raman spectrum acquisition module is to place the tissue slice on a Raman chip, observe the tissue sample under a Raman microscope, and collect the Raman spectrum of microorganisms and tissue cells.

[0029] The tissue sample single-cell sorting module is to position a laser spot to a cell to be sorted, eject the cell into a receptacle for standby, and complete the sorting.

[0030] The tissue sample single-cell lysis module is to lyse the sorted single cell in a lysis solution to obtain a cell lysis product.

[0031] The single-cell in situ sequencing module is to complete genome and / or transcriptome sequencing by whole-genome amplification reaction system amplification and library construction sequencing; when the sequenced single cell is used for biological species identification, genome sequencing and / or transcriptome sequencing are preferred; when the sequenced single cell is used to explain the interaction between the host and the microorganism, transcriptome sequencing is preferred.

[0032] Further, the thickness of the tissue slice is 5-10 μm, preferably 7-8 μm.

[0033] Further, the laser wavelength in the Raman spectrum acquisition is 525-550 nm, preferably 532-535 nm.

[0034] Further, the Raman spectrum acquisition time is 2-8 s, preferably 5-6 s.

[0035] Further, the laser intensity in the laser spot positioning is 1-5 mW, preferably 3-4 mW.

[0036] Further, the laser energy is 55-70nj, preferably 60-65nj.

[0037] Further, the lysis time is 8-15min, preferably 10-12min.

[0038] Further, the sample to be tested includes sources from pathological tissues, body fluids, food, drugs, cosmetics or environment, etc.

[0039] Further, the Raman spectrum sorting is Raman ejection sorting (RACE).

[0040] In a third aspect, the application provides an application of a single-cell tissue in-situ sequencing analysis method based on Raman spectrum in biological species identification, wherein the method is as described in the first aspect of the application.

[0041] In a fourth aspect, the application provides an application of a single-cell tissue in-situ sequencing analysis method based on Raman spectrum in microorganism and host interaction research, wherein the method is as described in the first aspect of the application. Advantages

[0042] (1) The tissue section required by the application does not need to be subjected to enzyme penetration, digestion, labeling and other steps, and can be directly subjected to Raman sorting, so the operation is simple, and the spatial position information of the microorganism is retained.

[0043] (2) The application does not require probe synthesis, labeling and fluorescence imaging, and the microorganism morphology can be directly observed under a microscope, and visual sorting can be performed.

[0044] (3) The application can accurately sort to a single microorganism cell in the tissue by collecting the Raman spectrum of the tissue cell and the Raman spectrum of the microorganism cell, and comparing the cell morphology with the spectrum.

[0045] (4) The application can perform genome sequencing of the single microorganism cell, and compared with the probe of the 16S rRNA conserved region, the application can identify the microorganism species at the species level.

[0046] (5) The application can perform transcriptome sequencing of the single microorganism cell, and through transcriptome analysis, the species can be identified, and the gene transcription change of the single microorganism cell in the host-microorganism interaction process can be revealed. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 is a schematic diagram of tissue sampling.

[0048] Fig. 2 is a comparison of the Raman spectrum of the bacterial single cell and the tissue cell of the intestinal cancer tissue.

[0049] Figure 3. Intestinal cancer tissue microorganism single cell sorting; (A) Microorganism Raman sorting result after 5 pm tissue section; (B) Microscope observation result of 10 pm thickness tissue section.

[0050] Figure 4. Sample whole genome amplification and sequencing result; (A) Tissue Raman microscope detection; (B) Bacterial genome map.

[0051] Figure 5. Tissue microorganism transcriptome sequencing; (A) Tissue Raman microscope detection; (B) Bacterial transcriptome species annotation, quality assessment and gene expression quantification. DETAILED DESCRIPTION

[0052] The specific embodiments of the present application are further described below. It is to be understood that the description of these embodiments is intended to help understand the present application and does not constitute a limitation of the present application. In addition, the technical features involved in the following described embodiments can be combined with each other as long as they do not conflict with each other.

[0053] The experimental methods in the following examples are all conventional methods unless otherwise specified. The experimental materials used in the following examples are all commercially available unless otherwise specified.

[0054] EMBODIMENT

[0055] Example 1 Sample collection and experimental method

[0056] Select patients with confirmed colorectal cancer, obtain tumor resection tissue by radical resection of colon cancer in patients; obtain colorectal cancer intestinal tissue pathological section.

[0057] Select intestinal cancer tissue at the rectal site, obtain the resected intestinal cancer tissue of the patient (Figure 1).

[0058] 1.1 Sample pretreatment

[0059] (1) Use dry gauze to wipe the tissue dry;

[0060] (2) Place the tissue on the tissue support and drop the optimal cutting temperature compound (OCT);

[0061] (3) Place the tissue on the freezing table and freeze until the embedding agent and the tissue freeze into white ice;

[0062] (4) Clamp the frozen tissue block on the microtome and select 2 pm, 3 pm, 4 pm, 7 pm and 10 pm section thickness respectively to cut out complete and smooth sections; for standby use.

[0063] 1.2 Tissue single cell Raman spectrum collection and sorting

[0064] (1) The above-mentioned spare tissue section is flattened and pasted on a Raman sorting chip;

[0065] (2) The tissue sample is placed under a Raman microscope for observation, the laser point is positioned to the cell to be tested, and Raman spectrum collection is performed;

[0066] (3) Raman collection conditions: laser wavelength is 532 nm, laser intensity on the sample is 3 mW, and Raman spectrum collection time is 5 s;

[0067] (4) Through cell morphology observation, cells or particles with a size not more than 10 μm are randomly selected (as shown in FIG. 3A and FIG. 4A), and after Raman spectrum collection, the selected particles are ejected into a PCR tube for subsequent library construction and sequencing. Through classification of sequencing reads and species identification of contigs after splicing of sequencing reads, it is determined whether the selected cells and particles are microorganisms (FIG. 2A). Cluster analysis is performed on the Raman spectrum of the tissue cells and the Raman spectrum of the microorganisms, so that the characteristic peaks (700-1700 nm) of the Raman spectra of the two can be compared, thereby determining the characteristic Raman spectrum of the microorganism (the part in the dashed line frame in FIG. 2B), so as to facilitate the probability of sorting of the microorganism single cell.

[0068] (5) The laser focus is aligned with the selected microorganism single cell, and 60 nJ is used to eject the selected cell into a receiver;

[0069] 1.3 Sample genome amplification and sequencing

[0070] (1) The laser focus is aligned with the selected microorganism single cell, and 60 nJ is used to eject the selected cell into a receiver; the receiver is inverted and placed in a PCR tube, and the cells are centrifuged into the PCR tube at 2000 rpm for 30 s, 3 μl of lysis solution (QIAGEN, item number: 150345) is added, and the single cell is lysed at 65°C for 10 min;

[0071] (2) 4 μl of PBS is added to the PCR tube to make the total volume 7 μl, and the single cell is lysed at 65°C for 10 min;

[0072] (3) 3 μl of stop solution is added, mixed, and stored at 4°C;

[0073] (4) Prepare a whole genome amplification reaction system (QIAGEN, #150345), and add 30 μl to the PCR tube; PCR reaction is performed at 30°C for 2.5-4 h;

[0074] (5) Perform library construction and sequencing on the PCR product according to the second-generation sequencing process;

[0075] (6) According to the sequencing results, sequence splicing, genome assembly, assembly quality evaluation and gene annotation are performed.

[0076] 1.4 Sample transcriptome amplification and sequencing

[0077] (1) The laser focus is aimed at the selected microbial single cell, and 60nJ is used to eject the selected cell into the receiver;

[0078] (2) 1.5 μl of lysis solution is placed in the receiver, and the single cell is ejected into the receiver, and then the receiver is inverted to a PCR tube, 2000 rpm, 30 s, and the cell is centrifuged to contain 2.5 μl of Lysis Buffer 24℃-10 min digestion, using liquid nitrogen repeated rapid freezing and thawing 3 times, adding 6.5 μl PBS PCR tube, and then adding 0.5 μl RNase inhibitor 24℃-10 min digestion, 95℃-3 min inactivation, 4℃ storage; wherein, the lysis solution is prepared according to the following components, and the pH is adjusted to 9, taking a total volume of 100 μl as an example:

[0079] (3) 2 μl of gDNA Wipeout Buffer is added, mixed, 42℃, 10 min to remove DNA;

[0080] (4) The reverse transcription master mix is prepared according to the following components, 42℃-60 min, 95℃-3 min, 4℃ storage;

[0081] (5) The master mix for ligation reaction is prepared according to the following components, 24℃-60 min, 95℃-3 min, 4℃ storage;

[0082] (6) The master mix for cDNA amplification reaction is prepared according to the following components, 30℃-2.5h, 65℃-5 min, 4℃ storage;

[0083] (7) The above amplification product is subjected to library sequencing.

[0084] 1.5 Experimental results

[0085] (1) Relationship between intestinal cancer tissue microbial single cell sorting and tissue section thickness

[0086] Microbial single cell sorting was performed using different thickness of tissue sections. The sequencing results of microbial single cells sorted under the condition of thickness below 5 pm (2 pm, 3 pm, 4 pm) showed that the sorted single cells could not be assembled to obtain a better quality genome, and only information at the family level could be obtained, with very poor integrity (Fig. 3A). When the tissue section was 10 pm thick, the collection of Raman spectra of microbial single cells inhabiting the tissue was affected, and the thick embedding agent interfered with the Raman spectrum signal, and it was also difficult to observe the microbial single cells (Fig. 3B). Therefore, too thick tissue sections are not conducive to the collection of spectral signals and the observation of microbial single cell morphology, affecting the identification of microbial single cells. In addition, too thick tissue sections are also not conducive to the ejection sorting of microbial single cells.

[0087] (2) Sequencing results of sample whole genome amplification

[0088] Based on the standard of bacterial and tissue cell Raman spectra established in Fig. 2, the sorted microbial single cells were detected by Raman spectrum and Raman microscope (Fig. 4A). The sorted microbial single cells were subjected to genome library construction, sequencing, and later data analysis, and the genome sequence of the sorted microbial single cells was obtained, with a completeness of 50% and a contamination of less than 10%. Through sequence alignment and species annotation, it was determined that the bacteria was Bacteroides intestinalis (Fig. 4B).

[0089] (3) Analysis of tissue microbial transcriptome sequencing results

[0090] At the same time, the sorted microbial single cells were subjected to transcriptome sequencing (Fig. 5A). The sorted single cells were subjected to transcriptome library construction, and after sequencing, the sequencing data was subjected to quality control, and host RNA and ribosome RNA were removed after pretreatment steps, and the sequencing data was further subjected to assembly, assembly and species annotation. By analyzing the quality controlled reads, it was found that the reads belonging to s__Bacteroides_xylanisolvens accounted for the highest proportion in the sequencing sample. Further, GTDB-tk analysis of the assembled transcriptome found that the transcriptome belonged to s_Bacteroides_xylanisolvens.

[0091] Through the quality evaluation of the transcriptome assembly data, it was found that the integrity of the assembled single cell genome was 68.15%, and the contamination was 8.06%, indicating that the quality of the assembled single cell transcriptome was good.

[0092] Further analysis was performed by assembled single cell transcriptome, and it was observed that 2756 Bacteroides related genes were detected. In addition, the expression of genes such as TonB-linked outer membrane protein, SusC RagA family, ABC transporter / permase, porin, which are essential for the nutrient absorption of s_Bacteroides_xylanisolvens in the intestinal tract and the reproduction of Bacteroides group, were also detected (Figure 5B). At the same time, the whole genome sequence of s_Bacteroides_xylanisolvens was downloaded from NCBI, and further analysis was performed by performing reference transcriptome analysis on the sequencing data, and it was found that 3616 genes of s_Bacteroides_xylanisolvens were detected in the sequencing data, including the above-mentioned key genes expressed by s_Bacteroides_xylanisolvens. These results further demonstrate the accuracy of the results obtained by the method of the present application.

[0093] These results demonstrate that Raman sorted tissue in situ single cell sequencing can accurately identify microbial species and gene expression of microorganisms.

Claims

1. A method for single-cell in situ sequencing analysis of tissue based on Raman spectroscopy, the method comprising the following steps: S01. tissue sample preparation, obtaining a tissue sample to be tested, and obtaining a tissue slice of a certain thickness by freezing, embedding, and slicing; S02. placing the tissue slice in step S01 on a Raman chip, observing the tissue sample under a Raman microscope, and collecting Raman spectra of microorganisms and tissue cells; S03. determining the corresponding single cells to be sorted in combination with the morphology of the tissue and microorganisms in the field of view of the Raman microscope; S04. positioning a laser spot to the cells to be sorted under the Raman microscope, ejecting the cells into a receptacle for standby, and completing the sorting; S05. transferring the single cells in the receptacle in step S04, lysing the cells to obtain cell lysates, and completing single-cell in situ sequencing of tissue by genome amplification, library construction, and sequencing; wherein the sequencing comprises genome sequencing and transcriptome sequencing; when the sequenced single cells are used for biological species identification, genome sequencing and / or transcriptome sequencing are preferred; when the sequenced single cells are used to elucidate the interaction between the host and the microorganisms, transcriptome sequencing is preferred.

2. A single-cell in-situ sequencing device, the device comprising a tissue sample processing module, a tissue sample single-cell Raman spectrum acquisition module, a tissue sample single-cell sorting module, a tissue sample single-cell lysis module, a single-cell in-situ sequencing module consisting of: the tissue sample processing module is to obtain a tissue sample to be tested, and obtain a tissue slice of a certain thickness by freezing, embedding, and slicing; the tissue sample single-cell Raman spectrum collection module is to place the tissue slice on a Raman chip, observe the tissue sample under a Raman microscope, and collect Raman spectra of microorganisms and tissue cells; the tissue sample single-cell sorting module is to position a laser spot to the cells to be sorted, eject the cells into a receptacle for standby, and complete the sorting; the tissue sample single-cell lysis module is to lyse the sorted single cells in a lysis solution to obtain cell lysates; the single-cell in situ sequencing module of tissue is to complete genome and / or transcriptome sequencing by whole-genome amplification reaction system amplification, library construction, and sequencing; when the sequenced single cells are used for biological species identification, genome sequencing and / or transcriptome sequencing are preferred; when the sequenced single cells are used to elucidate the interaction between the host and the microorganisms, transcriptome sequencing is preferred.

3. Use of the method for single-cell in situ sequencing analysis of tissue based on Raman spectroscopy according to claim 1 in biological species identification.

4. Use of the method for single-cell in situ sequencing analysis of tissue based on Raman spectroscopy according to claim 1 in the study of the interaction between microorganisms and the host.

5. The thickness of the tissue slice according to any one of claims 1-4 is 5-10 μm, preferably 7-8 μm.

6. In the Raman spectrum collection according to any one of claims 1-4, the laser wavelength is 525-550 nm, preferably 532-535 nm; and the Raman spectrum collection time is 2-8 s, preferably 5-6 s.

7. In the laser spot positioning according to any one of claims 1-4, the laser intensity is 1-5 mW, preferably 3-4 mW; and the laser energy is 55-70 nj, preferably 60-65 nj.

8. The lysis time according to any one of claims 1-4 is 8-15 min, preferably 10-12 min.

9. The sample under test according to any one of claims 1 to 4, wherein the sample under test is derived from a pathological tissue, a body fluid, a food, a pharmaceutical product, a cosmetic product, or an environment.

10. The Raman spectroscopy according to any one of claims 1 to 4, wherein the Raman spectroscopy is Raman ejection sorting (RACE).

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