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

Abstract

The invention relates to the technical field of microorganism single-cell sequencing, in particular to a single-cell tissue in-situ sequencing method based on a Raman spectrum sorting technology. According to the method, position information of microbial single cells in tissues is reserved through tissue slices, the microbial single cells of the tissues in the slices are identified and sorted based on Raman spectra, required target single cells are obtained, labeling is performed without depending on a probe and the like, and therefore in-situ detection of the single-cell tissue level is achieved. And obtaining single-cell genome and single-cell transcriptome information. The required tissue slice does not need the steps of enzyme penetration, digestion, marking and the like, the microbial form is directly observed under a microscope, Raman sorting is carried out, and the operation is simple; by combining cell morphology and spectrum comparison, tissue microorganism single cells are accurately sorted, species are identified through transcriptome analysis, and microorganism single cell level gene transcription changes in the host-microorganism interaction process can be revealed.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial single-cell sequencing, and in particular to a single-cell tissue in situ sequencing technology based on Raman spectroscopy sorting. Background Art

[0002] Raman spectroscopy is a scattering spectrum, which is the phenomenon that the frequency of incident light changes after being scattered when the molecular bonds in the compound are excited to the virtual energy state but have not yet recovered to the original state. Each single-cell Raman spectrum consists of more than 1,500 Raman peaks corresponding to a type of chemical bond. The peaks can be used as a "molecular fingerprint" unique to a single cell, and then 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 under a specific spatiotemporal state is called a "Raman group". The Raman group can quickly and inexpensively measure and monitor the "metabolome" with single-cell accuracy, and its changes can reflect and characterize the panoramic and almost infinite "state" and "function" of the cell system. In addition, water molecules do not have strong signal interference in the key fingerprint area, which enables it to have the ability to detect in vivo. Therefore, Raman group technology has almost all the characteristics required for ideal single-cell phenotyping or sorting technology, and is an ideal tool for single-cell phenotype group (phenome) identification. The spontaneous Raman signals of most cell phenotypes in nature are weak. In order to steadily improve the sensitivity of Raman detection, surface enhanced Raman probe technology based on labeling strategy has been developed. By detecting the strong Raman signal of SERS probe, targeted detection of living tumors, polysaccharide detection in living cells, specific detection of glycosylated proteins, telomere length assessment, 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 phenotypic recognition ability of single-cell Raman spectroscopy technology, a series of Raman activated cell sorting platforms (Ramanactivated celsorting, RACS) have been developed.

[0003] As a label-free, non-invasive, real-time and rapid phenotypic detection method, Raman spectroscopy can provide a large amount of information such as molecular conformation, electron distribution and the interaction between molecular forces. The "single-cell Raman map" composed of the Raman signals of all compounds in the cell can be used as its "chemical fingerprint" to distinguish and identify cell species, with the advantages of being fast, sensitive and non-destructive.

[0004] The interaction and assembly between cells depend on the three-dimensional structure in which the cells are located. The spatial position of cells and the characteristics of the cells themselves are crucial to determining how tissues function or fail when diseases occur; spatial omics combines the spatial position information of cells in tissues while measuring cell gene expression and cell gene structure, thereby interpreting the communication between cells, the structure and function of tissues, and the mechanism of disease occurrence. However, microbial communities have rich taxonomic diversity and spatial organization, and existing technologies make it difficult to simultaneously perform high taxonomic resolution and high spatial resolution microbial community analysis (Z.Cao et al., Spatial profiling of microbial communities by sequentialFISH with error-robust encoding.Nat Commun 14,1477(2023).). At present, researchers have mainly made the following attempts at tissue-localized microbial spatial omics:

[0005] (1) HiPR-FISH (high-phylogenetic-resolution microbiome mapping by fluorescence in situ hybridization): HiPR-FISH uses fluorescence in situ hybridization, binary coding, spectral imaging and machine learning decoding to achieve spatial localization and species identification of hundreds of microbial species in complex communities (H. Shi et al., Highly multiplexed spatial mapping of microbial communities. Nature 588, 676-681 (2020).). First, a probe is designed based on the 16S rRNA sequence of the target microorganism. The probe contains a 16S rRNA sequence that specifically binds to the target microorganism and a fluorescent dye (which can have multiple fluorescent groups). Through the combination of multiple dye groups, the sample can be imaged in 1023 unique combinations on a standard confocal microscope, and the spectral data under different excitation wavelengths can be spliced ​​into a spectral barcode. Therefore, this technology can identify hundreds of microorganisms at the genus level, perform spatial localization at the single cell level, and perform statistics on microbial abundance by performing FISH hybridization with synthetic probes. SEER-FISH (sequential error-robust fluorescence in situ hybridization), which is similar to the principle of HiPR-FISH, can perform multiple rounds of fluorescence imaging to identify microbial groups.

[0006] (2) MaPS-seq (metagenomic plot sampling by sequencin): Although FISH imaging-based microbiome analysis technology can retain the location information of microbial communities, this method has defects such as the need to pre-design probes and low resolution. MaPS-seq is a new technology that can analyze the spatial composition of tissue microbiota at the micrometer scale. MaPS-seq fixes microbiome samples in a gel matrix and breaks the tissue into particles by freezing; uses a droplet encapsulation method to combine barcoded 16S rRNA amplification primers with microorganisms in particle taxonomic units and perform deep sequencing; data analysis reveals heterogeneous distribution and positive and negative correlations between different microbial taxonomic units (RU 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 achieves multi-omics analysis by performing histology, tissue cell spatial RNA sequencing, and tissue microbial spatial 16S rRNA sequencing on a glass array, while simultaneously capturing and analyzing host mRNA and bacterial 16S RNA sequences. The main steps of this method are as follows: first, prepare tissue sections; prepare a glass array containing specific markers to simultaneously capture the variable regions of host mRNA and 16S rRNA; enzymatically permeabilize the cells to capture host mRNA and bacterial 16S RNA sequences; synthesize cDNA on the array surface, quantify the released cDNA material by qPCR, and use Illumina indexing to build a library and sequence the 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 regions of microbial 16SrRNA for microbial identification. Therefore, none of the above sequencing methods belong to single-cell sequencing methods for tissue microbial space. Summary of the invention

[0009] The present invention retains the position information of microbial single cells in tissues through tissue sections, identifies and sorts microbial single cells in the tissues in the sections based on Raman spectroscopy, obtains the desired target single cells, and does not rely on probes etc. for labeling, thereby achieving in situ detection of single-cell tissue levels and obtaining single-cell genome and single-cell transcriptome information. On this basis, the present invention is completed.

[0010] In a first aspect, the present invention provides a single cell tissue in situ sequencing analysis method based on Raman spectroscopy, the method comprising the following steps:

[0011] S01. Prepare tissue slice samples, obtain tissue samples to be tested, and obtain tissue slices of a certain thickness through freezing, embedding, and slicing;

[0012] S02. placing the tissue slice in step S01 on a Raman chip, observing the tissue sample under a Raman microscope, and collecting Raman maps of microorganisms and tissue cells;

[0013] S03. Determine the corresponding single cells to be sorted by combining the morphology of tissues and microorganisms in the field of view of the Raman microscope;

[0014] S04. Positioning the laser spot on the cells to be sorted under the Raman microscope, ejecting the cells into the receiver for standby use, and completing the sorting;

[0015] S05. After transferring the single cell in the receiver in the above step S04, the cell is lysed to obtain a cell lysate, and single-cell tissue in situ sequencing is completed through genome amplification, library construction and sequencing.

[0016] Furthermore, 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 is preferred; when the sequenced single cell is used to explain the interaction between host and microorganism, transcriptome sequencing is preferred.

[0017] Furthermore, the thickness of the tissue section is 5-10 μm, preferably 7-8 μm.

[0018] Furthermore, the laser wavelength in the Raman spectrum collection is 525-550nm, preferably 532-535nm.

[0019] Furthermore, the Raman spectrum acquisition time is 2-8 s, preferably 5-6 s.

[0020] Furthermore, the laser intensity in the laser point positioning is 1-5 mW, preferably 3-4 mW.

[0021] Furthermore, the laser energy is 55-70 nj, preferably 60-65 nj.

[0022] Furthermore, the lysis time is 8-15 min, preferably 10-12 min.

[0023] Furthermore, the samples to be tested include those from pathological tissues, body fluids, food, medicines, cosmetics or the environment.

[0024] Furthermore, the Raman spectroscopy sorting is Raman spectroscopy sorting by ejection (RACE).

[0025] In a second aspect, the present invention provides a single-cell tissue 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 tissue in situ sequencing module, wherein:

[0026] The tissue sample processing module is as follows: obtaining a tissue sample to be tested, and obtaining a tissue slice of a certain thickness through freezing, embedding, and slicing;

[0027] The tissue sample single cell Raman spectrum acquisition module is as follows: the tissue slice is placed on a Raman chip, the tissue sample is observed under a Raman microscope, and the Raman spectrum of microorganisms and tissue cells is acquired;

[0028] The tissue sample single cell sorting module is as follows: positioning the laser point to the cell to be sorted, ejecting the cell into a receiver for standby use, and completing the sorting;

[0029] The tissue sample single cell lysis module is used to lyse the sorted single cells in a lysis solution to obtain cell lysis products;

[0030] The single-cell tissue in situ sequencing module is: to complete genome and / or transcriptome sequencing through amplification and library construction and sequencing by a whole genome amplification reaction system; 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 explain the interaction between hosts and microorganisms, transcriptome sequencing is preferred.

[0031] Furthermore, the thickness of the tissue section is 5-10 μm, preferably 7-8 μm.

[0032] Furthermore, the laser wavelength in the Raman spectrum collection is 525-550nm, preferably 532-535nm.

[0033] Furthermore, the Raman spectrum acquisition time is 2-8 s, preferably 5-6 s.

[0034] Furthermore, the laser intensity in the laser point positioning is 1-5 mW, preferably 3-4 mW.

[0035] Furthermore, the laser energy is 55-70 nj, preferably 60-65 nj.

[0036] Furthermore, the lysis time is 8-15 min, preferably 10-12 min.

[0037] Furthermore, the samples to be tested include those from pathological tissues, body fluids, food, medicines, cosmetics or the environment.

[0038] Furthermore, the Raman spectroscopy sorting is Raman spectroscopy sorting by ejection (RACE).

[0039] In a third aspect, the present invention provides an application of a single-cell tissue in situ sequencing analysis method based on Raman spectroscopy in biological species identification, wherein the method is as described in the first aspect of the present invention.

[0040] In a fourth aspect, the present invention provides an application of a single-cell tissue in situ sequencing analysis method based on Raman spectroscopy in the study of microbial-host interactions, wherein the method is as described in the first aspect of the present invention.

[0041] Beneficial Effects

[0042] (1) The tissue sections required by the present invention do not need to go through the steps of enzyme penetration, digestion, labeling, etc., and can be directly subjected to Raman sorting, so the operation is simple and the spatial location information of the microorganisms is retained.

[0043] (2) The present invention does not require probe synthesis, labeling and fluorescence imaging, and the morphology of microorganisms can be directly observed under a microscope, allowing for visual sorting.

[0044] (3) The present invention collects Raman spectra of tissue cells and microbial cells, combines cell morphology with spectral comparison, and accurately sorts tissue microbial single cells.

[0045] (4) The present invention can perform genome sequencing of single microbial cells. Compared with the probe of the conserved region of 16S rRNA, the present invention can identify microbial species at the species level.

[0046] (5) The present invention can perform transcriptome sequencing of microbial single cells, identify species through transcriptome analysis, and reveal changes in gene transcription at the microbial single cell level during host-microbe interactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 .Schematic diagram of tissue sampling.

[0048] Figure 2 .Comparison of Raman spectra of bacterial single cells and tissue cells in colorectal cancer tissue.

[0049] Figure 3 .Single-cell sorting of microorganisms in colorectal cancer tissue; (A) Raman sorting results of microorganisms after tissue sections below 5μm; (B) Microscope observation results of 10μm thick tissue sections.

[0050] Figure 4 .Sample whole genome amplification and sequencing results; (A) tissue Raman microscopy detection; (B) bacterial genome map.

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

[0052] The specific embodiments of the present invention are further described below. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the embodiments described below 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 conventional methods unless otherwise specified, and the experimental materials used in the following examples are commercially available unless otherwise specified.

[0054] Example

[0055] Example 1 Sample Collection and Test Methods

[0056] Patients with confirmed colorectal cancer were selected, and tumor resection tissue was obtained through radical resection of colon cancer; pathological sections of colorectal cancer intestinal tissue were obtained.

[0057] The colorectal cancer tissues were selected from the rectum and the colorectal cancer tissues removed from the patients were obtained ( Figure 1 ).

[0058] 1.1 Sample pretreatment

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

[0060] (2) Place the tissue on a tissue support and add an optimal cutting temperature compound (OCT).

[0061] (3) Freeze the tissue on a freezing table until the embedding medium and tissue freeze into a white ice body;

[0062] (4) Clamp the frozen tissue block on the slicer and select slice thicknesses of 2 μm, 3 μm, 4 μm, 7 μm, and 10 μm, respectively, to cut complete and smooth slices for later use.

[0063] 1.2 Tissue single cell Raman spectroscopy acquisition and sorting

[0064] (1) Flattening the above-mentioned spare tissue slices and pasting them on the Raman sorting chip;

[0065] (2) placing the tissue sample under a Raman microscope to observe the sample, positioning the laser point on the cell to be tested, and collecting the Raman spectrum;

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

[0067] (4) By observing cell morphology, randomly select cells or particles with a size not exceeding 10 μm (e.g. Figure 3 A and Figure 4 A), after collecting the Raman spectrum, the selected particles are ejected into the PCR tube for subsequent library construction and sequencing. By classifying the sequencing reads and identifying the contig species after splicing the sequencing reads, it is determined whether the selected cells and particles are microorganisms ( Figure 2 A). Cluster analysis is performed on the Raman spectra of tissue cells and microorganisms, so that the characteristic peaks (700-1700nm) of the Raman spectra of the two can be compared to determine the characteristic Raman spectra of microorganisms ( Figure 2 B dotted box part), which is beneficial to increase the probability of single microbial cells being sorted.

[0068] (5) Focus the laser on the selected microbial single cell and eject the selected cell into the receiver at 60 nJ;

[0069] 1.3 Sample genome amplification and sequencing

[0070] (1) Focus the laser on the selected microbial single cell and eject the selected cell into the receiver at 60 nJ; put the receiver upside down on the PCR tube, centrifuge the cell into the PCR tube at 2000 rpm for 30 s, add 3 μl lysis buffer (QIAGEN, catalog number: 150345), and lyse the single cell at 65°C for 10 min;

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

[0072] (3) Add 3 μl stop solution, mix well and store at 4°C;

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

[0074] (5) Build a library and sequence the PCR product according to the next-generation sequencing process;

[0075] (6) Based on the sequencing results, sequence splicing, genome assembly, assembly quality assessment and gene annotation are performed.

[0076] 1.4 Sample transcriptome amplification and sequencing

[0077] (1) Focus the laser on the selected microbial single cell and eject the selected cell into the receiver at 60 nJ;

[0078] (2) Place 1.5 μl of lysis buffer in a receiver and eject a single cell into the receiver. Then, turn the receiver upside down and place the PCR tube. Centrifuge the cells at 2000 rpm for 30 seconds to a 2.5 μl Lysis Buffer for digestion at 24°C for 10 minutes. Repeat rapid freezing and thawing with liquid nitrogen for 3 times. Add the cells to a 6.5 μl PBS PCR tube. Add 0.5 μl RNase inhibitor for digestion at 24°C for 10 minutes. Inactivate at 95°C for 3 minutes and store at 4°C. The lysis buffer is prepared according to the following components. The pH is adjusted to 9. Take a total volume of 100 μl as an example:

[0079] Component Volume(μl) Final concentration 5% SDS 10 0.05％ 0.5MTris-HCl 10 0.05M β-mercaptoethanol 1 1％ 100mM EDTA 10 10mM 5MNaCl 10 0.5M 100mMsodiumpyrophosphate 10 10mM 100mM KCl 10 10mM RNase-free H2O 39

[0080] (3) Add 2 μl gDNA Wipeout Buffer, mix well and incubate at 42°C for 10 min to remove DNA;

[0081] (4) Prepare reverse transcription master mix according to the following composition, 42°C ~ 60 min, 95°C ~ 3 min, and store at 4°C;

[0082] Component Volume(μl) RT / PolymeraseBuffer 4 RandomPrimer 1 OligodTPrimer 1 QuantiscriptRTEnzymeMix 1 Total volume 7

[0083] (5) Prepare the master mix for the ligation reaction according to the following composition, 24°C for 60 min, 95°C for 3 min, and store at 4°C;

[0084] Component Volume(μl) LigationBuffer 8 LigaseMix 2 Total volume 10

[0085] (6) Prepare a cDNA amplification master mix according to the following composition, 30°C for 2.5 hours, 65°C for 5 minutes, and store at 4°C;

[0086] Component Volume(μl) REPLI-gscReactionBuffer 29 REPLI-gSensiPhiDNAPolymerase 1 Total volume 30

[0087] (7) Construct a library and sequence the amplified products.

[0088] 1.5 Experimental Results

[0089] (1) Relationship between single-cell sorting of colon cancer tissue microorganisms and tissue section thickness

[0090] Microbial single cell sorting was performed using tissue sections of different thicknesses. The sequencing results of microbial single cells sorted at a thickness below 5 μm (2 μm, 3 μm, 4 μm) showed that the sorted single cells could not be spliced ​​to obtain a genome of good quality, and only family-level information could be obtained, with very poor completeness ( Figure 3 A). When the tissue section is 10 μm, the collection of Raman spectra of single cells of microorganisms inhabiting the tissue will be affected. The overly thick embedding agent interferes with the Raman spectral signal and it is also difficult to observe single cells of microorganisms ( Figure 3 B). 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 not conducive to the ejection sorting of microbial single cells.

[0091] (2) Sample whole genome amplification and sequencing results

[0092] based on Figure 2 The Raman spectra of bacteria and tissue cells were standardized, and single microbial cells were sorted for detection by Raman spectroscopy and Raman microscopy. Figure 4 A). The genome library of the sorted microbial single cells was constructed, sequenced, and the subsequent data analysis was performed to obtain the genome sequence of the sorted microbial single cells. The completeness of the genome was 50% and the contamination was less than 10%. Through sequence alignment and species annotation, the bacterium was determined to be Bacteroides intestinalis ( Figure 4 B).

[0093] (3) Analysis of tissue microbial transcriptome sequencing results

[0094] At the same time, transcriptome sequencing was performed on the sorted microbial single cells ( Figure 5 A). The sorted single cells were subjected to transcriptome library construction. After sequencing, the sequencing data was quality controlled. After pre-processing steps such as removing host RNA and ribosomal RNA, the sequencing data was further spliced, assembled, and annotated. By analyzing the reads after quality control, it was found that the reads belonging to s__Bacteroides_xylanisolvens accounted for the highest proportion in the sequenced samples. Furthermore, GTDB-tk analysis of the transcriptome obtained after assembly revealed that the transcriptome belonged to s_Bacteroides_xylanisolvens.

[0095] By evaluating the quality of the transcriptome assembly data, it was found that the completeness 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.

[0096] Further analysis was performed through the assembled single-cell transcriptome, and 2756 Bacteroides-related genes were observed. In addition, the expression of genes such as TonB-linked outer membrane protein, SusC RagA family, ABCtransporter / permase, porin, etc., which are essential for the nutrient absorption of s_Bacteroides_xylanisolvens in the intestine and the reproduction of Bacteroidetes, was also detected ( Figure 5 B). At the same time, the whole genome sequence of s_Bacteroidesxylanisolvens was downloaded through NCBI, and the sequencing data was further analyzed with reference transcriptome analysis, and it was found that 3616 genes above s_Bacteroides_xylanisolvens were detected in the sequencing data, including the key genes expressed by the above-mentioned s_Bacteroides_xylanisolvens. These results further show the accuracy of the results obtained by the present application method.

[0097] These results demonstrate that Raman-sorted in situ single-cell sequencing of tissues can accurately identify microbial species and microbial gene expression.

Claims

1. A single cell tissue in situ sequencing analysis method based on Raman spectroscopy, the method comprising the following steps: S01. Prepare tissue slice samples, obtain tissue samples to be tested, and obtain tissue slices of a certain thickness through 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 maps of microorganisms and tissue cells; S03. Determine the corresponding single cells to be sorted by combining the morphology of tissues and microorganisms in the field of view of the Raman microscope; S04. Positioning the laser spot on the cells to be sorted under the Raman microscope, ejecting the cells into the receiver for standby use, and completing the sorting; S05. After transferring the single cell in the receiver in the above step S04, the cell is lysed to obtain a cell lysis product, and the single cell tissue in situ sequencing is completed through genome amplification, library construction and sequencing; wherein, the sequencing includes genome sequencing and transcriptome sequencing; when the sequenced single cell is used for biological species identification, genome sequencing and / or transcriptome sequencing is preferred; when the sequenced single cell is used to explain the interaction between host and microorganism, transcriptome sequencing is preferred.

2. A single-cell tissue 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, and a single-cell tissue in situ sequencing module Composition, of which: The tissue sample processing module is as follows: obtaining a tissue sample to be tested, and obtaining a tissue slice of a certain thickness through freezing, embedding, and slicing; The tissue sample single cell Raman spectrum acquisition module is as follows: the tissue slice is placed on a Raman chip, the tissue sample is observed under a Raman microscope, and the Raman spectrum of microorganisms and tissue cells is acquired; The tissue sample single cell sorting module is as follows: positioning the laser point to the cell to be sorted, ejecting the cell into a receiver for standby use, and completing the sorting; The tissue sample single cell lysis module is used to lyse the sorted single cells in a lysis solution to obtain cell lysis products; The single-cell tissue in situ sequencing module is: to complete genome and / or transcriptome sequencing through amplification and library construction and sequencing by a whole genome amplification reaction system; 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 explain the interaction between hosts and microorganisms, transcriptome sequencing is preferred.

3. An application of the single-cell tissue in situ sequencing analysis method based on Raman spectroscopy as claimed in claim 1 in biological species identification.

4. An application of the single-cell tissue in situ sequencing analysis method based on Raman spectroscopy as claimed in claim 1 in the study of microbial-host interaction.

5. The tissue section according to any one of claims 1 to 4 has a thickness of 5-10 μm, preferably 7-8 μm.

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

7. The laser intensity in the laser point positioning as described in any one of claims 1 to 4 is 1-5 mW, preferably 3-4 mW; the laser energy is 55-70 nj, preferably 60-65 nj.

8. The lysis time as described in any one of claims 1 to 4 is 8 to 15 min, preferably 10 to 12 min.

9. The sample to be tested as claimed in any one of claims 1 to 4 comprises a sample from a pathological tissue, a body fluid, a food, a medicine, a cosmetic or the environment.

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

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