Method and apparatus for microbiome information mining on spatial transcriptome data

Abstract

This invention provides a method and apparatus for mining microbial information from spatial transcriptome data. The method includes: acquiring spatial transcriptome data of a sample object; matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object; and determining the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to a normal object. The method of this invention can accurately determine the microbial information that differs between the sample object and the normal object, thus realizing the mining of microbial information in the sample object.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a method and apparatus for microbial information mining from spatial transcriptome data. Background Technology

[0002] Genomics-based research indicates that most major types of humans and objects contain microbiomes. Specific bacteria within these microbial communities can lead to the occurrence and development of risks; for example, specific bacteria in fruits, foods, and the environment can cause food spoilage and mold growth.

[0003] Currently, related technologies still cannot capture microbial information from the spatial transcriptome data of sample objects, thus making it impossible to determine the role of microorganisms in the host and their impact on the host based on this information. Therefore, how to accurately mine and identify microbial information in sample objects, and thus determine their role in the host and their impact on the host, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for microbial information mining of spatial transcriptome data.

[0005] Specifically, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, embodiments of the present invention provide a method for mining microbial information, comprising:

[0007] Obtain spatial transcriptome data of the sample objects;

[0008] All payload sequences in the transcriptome data are matched with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object;

[0009] Based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, the microbial information that differs between the sample object and the normal object is determined.

[0010] Further, the step of matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object includes:

[0011] All payload sequences in the transcriptome data are matched with preset microbial reference genomes and host reference genomes to determine the target payload sequence at the corresponding location of the sample object; the target payload sequence is the payload sequence that successfully matches the microbial reference genome and the payload sequence that successfully matches the host reference genome.

[0012] Based on the target payload sequence, determine the microbial information and gene expression information of the corresponding location of the sample object.

[0013] Further, determining the microbial information and gene expression information at the corresponding location of the sample object based on the target payload sequence includes:

[0014] Based on the microbial type and spatial sampling site corresponding to each target payload sequence, determine the type of microorganism contained in each spatial sampling site and the number of UMIs corresponding to each microbial type;

[0015] The types of microorganisms contained in each of the spatial sampling sites and the number of UMIs corresponding to each microorganism type are used as the microbial information of the corresponding location of the sample object.

[0016] Further, the step of determining the microbial information showing differences between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object includes:

[0017] The microbial information corresponding to the sample object is compared with the microbial information corresponding to the normal object to obtain the types of microorganisms added in each spatial sampling point corresponding to the sample object;

[0018] Based on the types of microorganisms added at each of the aforementioned spatial sampling points, the microbial information that indicates a difference between the sample object and the normal object is determined.

[0019] Furthermore, after matching multiple payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object, the method further includes:

[0020] Based on the payload sequence that successfully matches the preset host reference genome, determine the number of UMIs of the corresponding host genes at each spatial sampling point;

[0021] The microbial information corresponding to the sample object is filtered based on the type of microorganism contained in each of the spatial sampling sites, the number of UMIs corresponding to each type of microorganism, and the number of UMIs of the host corresponding to each of the spatial sampling sites.

[0022] Further, after determining the microbial information indicating differences between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, the method further includes:

[0023] Determine the spatial distribution characteristics of different types of microorganisms in the sample; and / or,

[0024] Determine the association between cell types and differentially expressed microbial types in the sample.

[0025] Secondly, embodiments of the present invention also provide an apparatus for microbial information mining of spatial transcriptome data, comprising:

[0026] The acquisition module is used to acquire spatial transcriptome data of sample objects;

[0027] The determination module is used to match all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object;

[0028] The mining module is used to determine the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object.

[0029] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for microbial information mining of spatial transcriptome data as described in the first aspect.

[0030] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for microbial information mining of spatial transcriptome data as described in the first aspect.

[0031] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the method for microbial information mining of spatial transcriptome data as described in the first aspect.

[0032] The method and apparatus for microbial information mining of spatial transcriptome data provided in this invention match all payload sequences in the spatial transcriptome data of a sample object with preset microbial reference genomes and host reference genomes to determine gene expression information and microbial information at each spatial sampling point in the sample object. Then, by comparing the microbial information corresponding to the sample object with that corresponding to a normal object, the differences in microbial information between the sample object and the normal object can be accurately determined, thereby identifying microbial species unique to the sample object and realizing the mining of microbial information in the sample object. Furthermore, by combining the cell type information contained in the spatial sampling points, the role of the microbial information in the sample object within the host and its impact on the host can be further analyzed. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a schematic flowchart of a method for mining microbial information from spatial transcriptome data provided in an embodiment of the present invention;

[0035] Figure 2 This is another flowchart illustrating the method for microbial information mining from spatial transcriptome data provided in this embodiment of the invention;

[0036] Figure 3 This is a schematic diagram of the device for microbial information mining of spatial transcriptome data provided in an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0039] The method of this invention can be applied to bioinformatics scenarios to achieve accurate and effective mining of microbial information in sample objects.

[0040] Currently, related technologies cannot capture microbial information from the spatial transcriptome data of sample objects, thus making it impossible to determine the role of microorganisms in the host and their impact on the host based on this information. Therefore, accurately mining and identifying microbial information in sample objects, and thus determining the role of specific microbial populations in the host and their impact on the host, is a technical problem that urgently needs to be solved by those skilled in the art.

[0041] The method for microbial information mining of spatial transcriptome data in this invention determines the microbial information in the sample by matching all payload sequences in the spatial transcriptome data of the sample with preset microbial reference genomes and host reference genomes. Then, the microbial information corresponding to the sample is compared with the microbial information corresponding to normal objects, which can accurately determine the microbial information that differs between the sample and normal objects, thereby identifying the microbial species unique to the sample and realizing the mining of microbial information in the sample. Furthermore, the cell type information contained in the spatial sampling points can be combined to further analyze the role of the microorganisms in the sample in the host and their impact on the host.

[0042] To facilitate a clearer understanding of the technical solutions of the various embodiments of this application, some technical content related to the various embodiments of this application will be introduced first.

[0043] Spatial transcriptomics technology captures mRNA transcripts at each spatial sampling point using microarray chips. Ideally, the abundance of gene transcripts at a given point would represent the expression level of that gene at that point. However, in reality, the information captured at each sampling point does not only represent the gene expression of the host at that spatial location; information about the microorganisms present at that location may also be captured by probes at the sampling point.

[0044] Currently, there is no method to directly capture microbial information from spatial transcriptome data.

[0045] The following is combined Figures 1-4 The technical solution of the present invention will be described in detail with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0046] Figure 1 This is a schematic flowchart of an embodiment of a method for microbial information mining from spatial transcriptome data provided by this invention. Figure 1 As shown, the method provided in this embodiment includes:

[0047] Step 101: Obtain spatial transcriptome data of the sample objects;

[0048] Specifically, spatial transcriptome data based on array chips contain RNA molecules from various sources. Among these, exploring the microbial population origin of RNA molecules from microorganisms within the target tissue's spatial sampling points is crucial for downstream microenvironment analysis. However, to date, data in spatial transcriptome data that have not been successfully matched to the host reference genome are considered to be erroneous sequences due to technical factors and have been excluded from downstream analysis.

[0049] To address the aforementioned issues, this embodiment of the invention first acquires the spatial transcriptome data of the sample object. This spatial transcriptome data includes object spatial information and transcriptome data. Compared to single-cell sequencing, spatial transcriptome technology compensates for the spatial location information lost during object dissociation in single-cell sequencing. Optionally, the spatial transcriptome data of the sample object includes sampling site encoding, a unique molecular identifier (UMI), and a payload sequence. Optionally, the sample object can be a human body part or other animal or plant object; optionally, the sample object can be animal or plant tissue.

[0050] Step 102: Match all payload sequences in the transcriptome data with the preset microbial reference genome and host reference genome to determine the microbial information corresponding to the sample object;

[0051] Specifically, in this embodiment of the invention, after obtaining the spatial transcriptome data of a sample object, all payload sequences in the spatial transcriptome data of the sample object can be matched with a preset microbial reference genome and a host reference genome to determine the microbial information and gene expression information at each spatial sampling point corresponding to the sample object. Optionally, the microbial reference genome includes microbial genomes such as fungi, bacteria, archaea, and viruses. Optionally, the payload sequences that successfully match the microbial reference genome are microbial-related sequences, and the microbial information corresponding to the sample object can be determined based on the microbial-related sequences. Optionally, the number of host genes contained in each spatial sampling site and the number of unique molecular identifiers (UMIs) corresponding to each gene are used as the gene expression information at the corresponding position of the sample object.

[0052] For example, the input data is a FASTQ file from spatial transcriptome sequencing of a sample object. Each sequence contains a sampling site code, a unique molecular identifier (UMI), and a payload sequence. By precisely matching and classifying the payload sequences using host and microbial reference genomes, microbial-related sequences and host-related sequences can be extracted from the FASTQ file based on the classification results. For instance, if payload sequence 1 successfully matches the microbial reference genome corresponding to fungal type 1, then payload sequence 1 can be determined to be a microbial-related sequence, and the microbial information corresponding to the sample object includes fungal type 1.

[0053] Step 103: Based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, determine the microbial information that differs between the sample object and the normal object. Specifically, after determining the sequences related to microorganisms in the sample object and the microbial information corresponding to the sample object, the microbial information and tissue states that differ between the sample object and the normal object can be determined based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object. Optionally, after determining the microbial information that differs between the sample object and the normal object, the interaction and association relationships between the microorganisms in the sample object and the tissue spatial locations where the microorganisms are located can be determined based on the gene expression and microbial information at each spatial location in the sample object. The method of this embodiment can accurately determine the microbial information that differs between the sample object and the normal object, and through association analysis with host tissue state and related host cell types, it realizes the mining of information on the interaction between microorganisms and host tissues in the sample object.

[0054] For example, if a sample contains information about fungal type 1, while a normal sample does not, then the information about fungal type 1 represents the differential microbial information between the sample and the normal sample. This is essentially a microbial species unique to the sample corresponding to the spatial transcriptome, thus achieving noise reduction of the microbial information in the sample. Furthermore, it allows analysis of the correlation between the microbial information (i.e., fungal type 1) and the characteristics of the sample. For instance, if the sample is fruit a, and it exhibits mold and spoilage, it's possible to further determine whether the mold or spoilage of fruit a is related to the presence of fungal type 1 within it. This helps identify and analyze the role of fungal type 1 within the host and its impact on the host. In other words, it allows for further analysis of the correlation between abnormal microbial information in the sample and the host gene expression status within the spatial sampling sites of the sample, determining the role of abnormal microbial information within the host and its impact on the host.

[0055] The method described above determines the microbial information in the sample object by matching all payload sequences in the spatial transcriptome data of the sample object with a preset microbial reference genome and a host reference genome. Then, by comparing the microbial information corresponding to the sample object with the microbial information corresponding to the normal object, the method can accurately determine the microbial information that differs between the sample object and the normal object, thereby identifying the microbial species unique to the sample object. This enables the mining of microbial information in the sample object, and further allows for the determination and analysis of the role played by abnormal microbial information in the sample object within the host and its impact on the host.

[0056] In one embodiment, all payload sequences in the transcriptome data are matched with a predefined microbial reference genome and a host reference genome to determine the microbial information corresponding to the sample object, including:

[0057] All payload sequences in the transcriptome data are matched with preset microbial reference genomes and host reference genomes to determine the target payload sequences corresponding to the sample objects; the target payload sequences are the payload sequences that successfully match the microbial reference genome and the payload sequences that successfully match the host reference genome.

[0058] Based on the target payload sequence, determine the microbial information and gene expression information of the corresponding location of the sample object.

[0059] Specifically, in this embodiment of the invention, all payload sequences in the spatial transcriptome data of the sample object are matched with preset microbial reference genomes and host reference genomes to determine the microbial information of each spatial sampling point in the sample object. Optionally, the target payload sequence corresponding to the sample object can be determined by matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes. The target payload sequence is the payload sequence that successfully matches the microbial reference genome and the payload sequence that successfully matches the host reference genome. That is, the payload sequence that successfully matches the microbial reference genome is taken as the microbial-related payload sequence in the spatial transcriptome data of the sample object, thereby realizing the accurate classification of the payload sequences in the transcriptome data of the sample object. Then, based on the classified microbial-related payload sequences, the microbial information and gene expression information in each spatial sampling point corresponding to the sample object can be accurately determined.

[0060] The method described in the above embodiments matches all payload sequences in the transcriptome data with a preset microbial reference genome and a host reference genome, and uses the payload sequences that successfully match the microbial reference genome as microbial-related payload sequences in the spatial transcriptome data of the sample object. This achieves accurate classification of payload sequences in the transcriptome data of the sample object, and then the microbial information corresponding to the sample object can be accurately determined based on the classified microbial-related payload sequences.

[0061] In one embodiment, determining the microbial information and gene expression information at the corresponding location of the sample object based on the target payload sequence includes:

[0062] Based on the microbial type and spatial sampling site corresponding to each target payload sequence, determine the type of microorganism contained in each spatial sampling site and the number of unique molecular identifiers (UMIs) corresponding to each microbial type.

[0063] The types of microorganisms contained in each spatial sampling site and the number of UMIs corresponding to the target payload sequences of each microorganism type are used as the microbial information of the corresponding location of the sample object.

[0064] The number of host genes and the number of unique molecular identifiers (UMIs) corresponding to each gene contained in each spatial sampling site are used as the gene expression information of the corresponding location of the sample object.

[0065] Specifically, in this embodiment of the invention, the payload sequences related to microorganisms in the spatial transcriptome data of the sample object are determined, that is, the payload sequences in the transcriptome data of the sample object are classified. After determining the target payload sequences, the microbial type and spatial sampling site corresponding to each target payload sequence are determined. Optionally, the spatial transcriptome data of the sample object includes sampling site codes, unique molecular identifiers (UMIs), and payload sequences. After determining the target payload sequences, the spatial sampling points corresponding to each target payload sequence can be determined according to the correspondence between the sampling site codes and payload sequences in the spatial transcriptome data of the sample object, that is, the spatial location information of the target payload sequences in the sample object can be determined. Optionally, the microbial type corresponding to the target payload sequence can be determined according to the reference genome of the microorganism that successfully matches the target payload sequence. For example, if the reference genome of the microorganism that successfully matches the target payload sequence 1 is the reference genome of fungal type 1, then the microbial type corresponding to the target payload sequence 1 can be determined to be fungal type 1.

[0066] Optionally, after determining the microbial type and spatial sampling site corresponding to each target payload sequence, the types of microorganisms contained in each spatial sampling site and the number of UMIs corresponding to each microbial type can be further determined. Optionally, after determining the microbial type and spatial sampling site corresponding to each target payload sequence, each spatial sampling point can be classified to determine the target payload sequence corresponding to each spatial sampling point. Optionally, a spatial sampling point may include multiple target payload sequences. Optionally, after determining the target payload sequences included in each spatial sampling point, the types of microorganisms contained in each spatial sampling point and the number of UMIs corresponding to each microbial type can be accurately determined based on the microbial type corresponding to each target payload sequence. Optionally, based on the target payload sequences corresponding to each microbial type contained in each spatial sampling point and the number of UMIs corresponding to each microbial type, the determined microbial information of the sample object can not only accurately reflect the type characteristics of microorganisms corresponding to each spatial location of the sample object, but also accurately reflect the abundance characteristics of each type of microorganism corresponding to each spatial location of the sample object, making the determined microbial information of the sample object richer and more comprehensive, thus realizing the accurate and comprehensive mining of microbial information in the sample object.

[0067] The method described in the above embodiments determines the types of microorganisms contained in each spatial sampling site and the number of UMIs corresponding to each microorganism type based on the microbial type and spatial sampling site corresponding to each target payload sequence. By using the types of microorganisms contained in each spatial sampling site and the number of UMIs corresponding to each microorganism type as the microbial information corresponding to the sample object, the determined microbial information of the sample object can not only accurately reflect the type characteristics of microorganisms corresponding to each spatial location of the sample object, but also accurately reflect the abundance characteristics of each type of microorganism in each spatial location of the sample object. This makes the determined microbial information of the sample object richer and more comprehensive, thus achieving accurate and comprehensive mining of microbial information in the sample object. Furthermore, based on the richer and more comprehensive microbial information mined, it is possible to further accurately analyze the role of abnormal microbial information in the sample object in the host and its impact on the host.

[0068] In one embodiment, based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, the microbial information that differs between the sample object and the normal object is determined, including:

[0069] The microbial information corresponding to the sample object is compared with the microbial information corresponding to the normal object to obtain the types of microorganisms added in each spatial sampling point corresponding to the sample object;

[0070] Based on the types of microorganisms added at each spatial sampling point, we can determine the microbial information that distinguishes the sample objects from normal objects.

[0071] Specifically, in this embodiment of the invention, after determining the microbial information corresponding to the sample object, the microbial information corresponding to the sample object can be compared with the microbial information corresponding to the normal object to obtain the types of microorganisms added in each spatial sampling point corresponding to the sample object, that is, to obtain the types of microorganisms unique to each spatial sampling point corresponding to the sample object. These unique types of microorganisms in each spatial sampling point corresponding to the sample object are used as the differential microbial information or abnormal microbial information of the sample object compared to the normal object. Optionally, the method for obtaining the microbial information corresponding to the normal object can be based on the method for obtaining microorganisms in the sample object. Optionally, in this embodiment of the invention, not only the types of microorganisms added in each spatial sampling point can be used as the differential microbial information of the sample object compared to the normal object, but also the number of microorganism-related UMIs added in each spatial sampling point can be used as the differential microbial information of the sample object compared to the normal object; that is, when determining the abnormal microbial information of the sample object compared to the normal object, not only the abnormality of the microbial types in the sample object is considered, but also the abnormality of the number of microorganism-related UMIs in the sample object is considered, thereby uncovering richer and more comprehensive abnormal microbial information of the sample object compared to the normal object.

[0072] For example, the spatial location X of the sample object includes microbial type A and microbial type B, with 50 UMIs related to microbial type A; while the spatial location X of the normal object only contains microbial type A, with 2 UMIs related to microbial type A; then the difference between the sample object's spatial location A and the normal object includes microbial type B, as well as the abnormal number of UMIs related to microbial type A.

[0073] The method described in the above embodiments, when determining the microbial information that differs between a sample object and a normal object, considers not only the abnormality of the microbial type in the sample object, but also the abnormality of the number of microbial-related microorganisms (UMIs) in the sample object. This allows for the discovery of richer and more comprehensive abnormal microbial information in the sample object compared to the normal object. Consequently, the correlation between the abnormal microbial information and the abnormal phenomena in the sample object can be determined more accurately. Based on the more accurate and comprehensive abnormal microbial information in the sample object, it is possible to more accurately analyze and determine the role it plays in the host and its impact on the host.

[0074] In one embodiment, after matching multiple payload sequences in the transcriptome data with a preset microbial reference genome and a host reference genome to determine the microbial information corresponding to the sample object, the method further includes:

[0075] Based on the payload sequence that successfully matches the preset host reference genome, determine the number of UMIs of the corresponding host genes at each spatial sampling point;

[0076] Based on the type of microorganisms contained in each spatial sampling site, the number of UMIs corresponding to each microorganism type, and the number of unique molecular identifiers (UMIs) of the host gene corresponding to each spatial sampling site, the microbial information corresponding to the sample object is filtered.

[0077] Specifically, after matching multiple payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object, in this embodiment of the invention, based on the payload sequences that successfully match the preset host reference genomes, the number of UMIs of each host gene corresponding to each spatial sampling site is determined. That is, based on the payload sequences compared to the host, the number of UMIs of each host gene matched to each spatial sampling site is counted. Then, based on the type of microorganism contained in each spatial sampling site, the number of UMIs corresponding to each microbial type, and the number of UMIs of the host corresponding to each spatial sampling site, low-quality microbial information corresponding to the sample object can be filtered. Optionally, as the number of spatial sampling sites increases, the type of microorganism contained in the spatial sampling site, the number of UMIs corresponding to each microbial type, and the number of UMIs of the host corresponding to each spatial sampling site should be positively correlated. Optionally, if as the number of spatial sampling sites increases, the type of microorganism contained in the spatial sampling site, the number of UMIs corresponding to each microbial type, and the number of UMIs of the host corresponding to each spatial sampling site are not positively correlated, it indicates that the spatial transcriptome data of the sample object is contaminated, and the contaminated data needs to be filtered out to improve the accuracy of the mined microbial information of the sample object. Optionally, the correlation between the number of UMIs corresponding to microbial types and the number of UMIs of the corresponding host in the spatial sampling point, the correlation between the number of microbial types contained in the spatial sampling point and the number of UMIs of the corresponding host in the spatial sampling point, and the correlation between the number of UMIs corresponding to microbial types and the number of microbial types contained in the spatial sampling point can be statistically analyzed. If the correlation of the above three in the spatial sampling point is poor, such as a correlation of less than 0.05, it is considered that the data is contaminated and the relevant microbial information is incorrect, and it needs to be excluded from downstream analysis.

[0078] The method described above determines whether there is a positive correlation between the types of microorganisms contained in each spatial sampling site, the number of UMIs corresponding to each microorganism type, and the number of UMIs of the host corresponding to each spatial sampling site. This accurately identifies whether the spatial transcriptome data of the sample object is contaminated, and improves the accuracy of the microbial information of the sample object by filtering out contaminated data.

[0079] In one embodiment, after determining the microbial information showing differences between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, the method further includes:

[0080] Determine the spatial distribution characteristics of different types of microorganisms in the sample; and / or,

[0081] Determine the association between cell types and differentially expressed microbial types in the sample.

[0082] Specifically, in this embodiment of the invention, after determining the microbial information that differs between the sample object and the normal object, the spatial distribution characteristics of each type of differentially expressed microorganism in the sample object are further determined; and / or, the correlation between cell types and differentially expressed microorganism types in the sample object is determined. This achieves in-depth mining of microbial information in the sample object. By mining the spatial distribution characteristics of each differentially expressed microorganism in the sample object, and the differences in the cell types to which the differentially expressed microorganisms attach, it is possible to determine the role played by each type of differentially expressed microorganism in the host and its impact on the host based on the spatial distribution characteristics of each type of differentially expressed microorganism and / or the correlation between cell types and differentially expressed microorganism types. Optionally, based on the coordinates of the spatial sampling sites, the spatial self-similarity between spatial sampling sites corresponding to the same microbial population can be calculated to determine microbial populations with spatial aggregation; alternatively, based on the cell types in the spatial sampling sites, the correlation between cell types and microbial population abundance in the spatial sampling sites corresponding to the same microbial population can be calculated to determine cell-specific microbial populations.

[0083] For example, if the sample is moldy or spoiled fruit, the method of this embodiment identifies microorganism 'a' that differs from normal samples. Microorganism 'a' is clustered at spatial locations x, y, and z of the sample. For instance, x, y, and z are three consecutive spatially distinct locations. This means that the clustered microorganism 'a' at spatial locations x, y, and z may be related to the fruit's mold or spoilage. Therefore, during the quality testing of other fruits, the presence of microorganism 'a' at spatial locations x, y, and z can be analyzed to further accurately determine if fruit m is at risk of mold or spoilage. Optionally, inferring the possible cell types present at x, y, and z through gene expression information can confirm whether there is a special relationship between microorganism 'a' and cell types. Optionally, the sample can also be cancerous lung tissue from a human body. After mining the differential microbial information present in the sample, further analysis can be conducted to determine whether there is a correlation between the abnormal microbial information in the sample and the lung cancer symptoms. Inferring the possible cell types present at corresponding locations in the lung tissue through gene expression information can confirm whether there is a correlation between abnormal microorganisms and cell types. By comparing gene expression with that of the same cell type where the microorganism is absent, the role of anomalous microbial information in the sample can be determined within the host and its impact on the host.

[0084] The method described in the above embodiments, by determining the spatial distribution characteristics of different types of microorganisms in the sample object; and / or determining the association between cell types and different types of microorganisms in the sample object, achieves in-depth mining of microbial information in the sample object. By mining the spatial distribution characteristics of each type of different microorganism in the sample object, as well as the differences in attached cell types and the differences in gene expression of the same cell types, it is possible to determine the role played by each type of different microorganism in the host and its impact on the host based on the spatial distribution characteristics of each type of different microorganism and / or the association between cell types and different types of microorganisms.

[0085] For example, the method for microbial information mining of spatial transcriptome data in this embodiment of the invention is as follows: Figure 2 As shown, the specific process is as follows:

[0086] (1) Using host and microbial reference genomes, each sequence in the spatial transcriptome is classified, and only microbial sequences that may originate from non-hosts are retained;

[0087] Optionally, spatial transcriptome data alignment can be performed, where the input data is a FASTQ file from spatial transcriptome sequencing, and each sequence contains a sampling site code, a unique molecular identifier (UMI), and a payload sequence; the payload sequences are precisely matched using host and microbial reference genomes, and then microbial-related sequences are extracted from the FASTQ file based on the classification results of the previous step.

[0088] (2) Based on the barcode of the spatial sampling site, trace the spatial location information of the microbial sequence;

[0089] Optionally, spatial tracing of microbial information is performed. Based on the extracted microbial-related sequences, the barcodes of the contained spatial sampling sites are located. A barcode statistics table of spatial sampling sites is constructed to record all sequences corresponding to each barcode. If two sequences are used for a common barcode and UMI, one of the sequences is discarded. Based on the previous classification of sequences, the species of microorganisms contained in the barcode of each spatial sampling site and the number of their corresponding sequences are counted.

[0090] (3) Inferring the types of cells present at the same sampling site using spatial transcriptome data;

[0091] Optionally, cell type identification is performed. Based on the sequence compared to the host, the number of host UMIs matched to each spatial sampling site is counted. Based on the comparison results of the payloads corresponding to the UMIs, the expression level information of each host gene within the spatial sampling site is constructed. Based on the gene expression profile information obtained in the previous step, combined with the prior cell type data of the relevant objects, the cell types that may be included within the spatial sampling site are inferred.

[0092] (4) Filter out false positive data by combining the sequencing depth of each cell with the number and abundance of microbial species between cells;

[0093] Optionally, based on the results of filtering and comparing information within the sample, the correlation between the number of microbial UMIs and the number of host UMIs, the correlation between the number of microbial species and the number of host UMIs, and the correlation between the number of microbial UMIs and the number of microbial species are calculated. If the correlation of spatial sampling sites is not significant in the above three steps, the relevant microbial information is considered to be incorrect and excluded from downstream analysis.

[0094] (5) Analyze cell lines of similar objects and identify their microbial information, and cross-validate it with the microbial information of the target object;

[0095] Optionally, microbial information can be extracted from the cell lines of normal subjects; the microbial information in the cell line transcriptome data of normal subjects and the spatial transcriptome data of sample subjects can be compared, and the differences are the microbial species unique to the sample subjects corresponding to the spatial transcriptome.

[0096] (6) Analyze the relationship between microbial populations, their abundance, spatial location, and cell type.

[0097] Optionally, an association analysis of cell type, spatial location, and microorganisms can be performed. Based on the coordinates of the spatial sampling sites, the spatial self-similarity between the spatial sampling sites corresponding to the same microbial population can be calculated to determine the microbial population with spatial aggregation. Based on the cell type in the spatial sampling sites, the correlation between the cell type and the abundance of the microbial population in the spatial sampling sites corresponding to the same microbial population can be calculated to determine the microbial population with cell type specificity.

[0098] The method described in the above embodiments uses spatial transcriptomics technology to re-compare data in spatial transcriptome data that does not belong to the host, thereby inferring its possible microbial origin. This invention proposes for the first time a method for mining microbial information from spatial transcriptome data. By associating it with spatial location and cell type, it can determine the spatial distribution characteristics of each microorganism in the target, the differences in attached cell types, and the differences in gene expression among attached cell types.

[0099] The apparatus for mining microbial information from spatial transcriptome data provided by the present invention will be described below. The apparatus for mining microbial information from spatial transcriptome data described below and the method for mining microbial information from spatial transcriptome data described above can be referred to in correspondence with each other.

[0100] Figure 3 This is a schematic diagram of the device for microbial information mining of spatial transcriptome data provided by the present invention. The device for microbial information mining of spatial transcriptome data provided in this embodiment includes:

[0101] The acquisition module 310 is used to acquire spatial transcriptome data of the sample object;

[0102] The determination module 320 is used to match all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object;

[0103] The mining module 330 is used to determine the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object.

[0104] The apparatus of the above embodiment determines the microbial information in the sample object by matching all payload sequences in the spatial transcriptome data of the sample object with preset microbial reference genomes and host reference genomes; then, by comparing the microbial information corresponding to the sample object with the microbial information corresponding to normal objects, it can accurately determine the microbial information that differs between the sample object and normal objects, thereby identifying the microbial species unique to the sample object, realizing the mining of microbial information in the sample object, and further determining and analyzing the role of abnormal microbial information in the sample object in the host and its impact on the host.

[0105] Optionally, the determining module 320 is specifically used to: match all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the target payload sequence at the corresponding position of the sample object; the target payload sequence is the payload sequence that successfully matches the microbial reference genome and the payload sequence that successfully matches the host reference genome;

[0106] Based on the target payload sequence, determine the microbial information corresponding to the location of the sample object.

[0107] Optionally, the determining module 320 is specifically used to: determine the type of microorganism contained in each spatial sampling site and the number of UMIs corresponding to each microorganism type based on the microbial type and spatial sampling site corresponding to each target payload sequence;

[0108] The types of microorganisms contained in each spatial sampling site and the number of UMIs corresponding to each microorganism type are used as the microbial information of the corresponding location of the sample object.

[0109] The number of host genes and the number of unique molecular identifiers (UMIs) corresponding to each gene contained in each spatial sampling site are used as the gene expression information of the corresponding location of the sample object.

[0110] Optionally, the mining module 330 is specifically used to: compare the microbial information corresponding to the sample object with the microbial information corresponding to the normal object, and obtain the types of microorganisms added in each spatial sampling point corresponding to the sample object;

[0111] Based on the types of microorganisms added at each spatial sampling point, determine the microbial information that distinguishes the sample object from the normal object.

[0112] Optionally, the determining module 320 is further configured to: determine the number of UMIs of the host gene at each spatial sampling point based on the payload sequence that successfully matches the preset host reference genome;

[0113] Based on the type of microorganisms contained in each spatial sampling site, the number of UMIs corresponding to each microorganism type, and the number of unique molecular identifiers (UMIs) of the host gene corresponding to each spatial sampling site, the microbial information corresponding to the sample object is filtered.

[0114] Optionally, the mining module 330 is further configured to: determine the spatial distribution characteristics of different types of microorganisms in the sample object; and / or,

[0115] Determine the association between cell types and differentially expressed microbial types at each spatial sampling point in the sample object.

[0116] The apparatus of this invention is used to execute the method in any of the foregoing method embodiments, and its implementation principle and technical effect are similar, so it will not be described again here.

[0117] Figure 4 A schematic diagram of the physical structure of an electronic device is provided. This electronic device may include a processor 410, a communication interface 420, a memory 430, and a communication bus 440. The processor 410, communication interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can invoke logical instructions in the memory 430 to execute a method for microbial information mining of spatial transcriptome data. This method includes: acquiring spatial transcriptome data of a sample object; matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object; and determining the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to a normal object.

[0118] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the method for microbial information mining of spatial transcriptome data provided by the above methods, the method comprising: acquiring spatial transcriptome data of a sample object; matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object; and determining the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to a normal object.

[0120] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the methods described above for mining microbial information from spatial transcriptome data. The method includes: acquiring spatial transcriptome data of a sample object; matching all payload sequences in the transcriptome data with a preset microbial reference genome and a host reference genome to determine the microbial information corresponding to the sample object; and determining, based on the microbial information corresponding to the sample object and the microbial information corresponding to a normal object, the microbial information that differs between the sample object and the normal object.

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for microbial information mining from spatial transcriptome data, characterized in that, include: Obtain spatial transcriptome data of the sample objects; All payload sequences in the transcriptome data are matched with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object; Based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, determine the microbial information that differs between the sample object and the normal object; After matching multiple payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object, the method further includes: Based on the payload sequence that successfully matches the preset host reference genome, determine the number of unique molecular identifiers (UMIs) of the corresponding host genes at each spatial sampling point. The microbial information corresponding to the sample object is filtered based on the type of microorganism contained in each of the spatial sampling sites, the number of unique molecular identifiers (UMIs) corresponding to each microbial type, and the number of unique molecular identifiers (UMIs) of the host gene corresponding to each spatial sampling site.

2. The method for microbial information mining of spatial transcriptome data according to claim 1, characterized in that, The step of matching all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object includes: All payload sequences in the transcriptome data are matched with preset microbial reference genomes and host reference genomes to determine the target payload sequence at the corresponding location of the sample object; the target payload sequence is the payload sequence that successfully matches the microbial reference genome and the payload sequence that successfully matches the host reference genome. Based on the target payload sequence, determine the microbial information and gene expression information of the corresponding location of the sample object.

3. The method for microbial information mining of spatial transcriptome data according to claim 2, characterized in that, The step of determining the microbial information and gene expression information at the corresponding location of the sample object based on the target payload sequence includes: Based on the microbial type and spatial sampling site corresponding to each target payload sequence, determine the type of microorganism contained in each spatial sampling site and the number of unique molecular identifiers (UMIs) corresponding to each microbial type; The types of microorganisms contained in each of the spatial sampling sites and the number of unique molecular identifiers (UMIs) corresponding to each type of microorganism are used as the microbial information of the corresponding location of the sample object. The number of host genes and the number of unique molecular identifiers (UMIs) corresponding to each gene contained in each spatial sampling site are used as the gene expression information of the corresponding location of the sample object.

4. The method for microbial information mining of spatial transcriptome data according to any one of claims 1-3, characterized in that, The step of determining the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object includes: The microbial information corresponding to the sample object is compared with the microbial information corresponding to the normal object to obtain the types of microorganisms added in each spatial sampling point corresponding to the sample object; Based on the types of microorganisms added at each of the aforementioned spatial sampling points, determine the microbial information that indicates a difference between the sample object and the normal object.

5. The method for microbial information mining of spatial transcriptome data according to claim 1, characterized in that, After determining the microbial information showing differences between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object, the method further includes: Determine the spatial distribution characteristics of different types of microorganisms in the sample; and / or, Determine the association between cell types and differentially expressed microbial types in the sample.

6. An apparatus for microbial information mining from spatial transcriptome data, characterized in that, include: The acquisition module is used to acquire spatial transcriptome data of sample objects; The determination module is used to match all payload sequences in the transcriptome data with preset microbial reference genomes and host reference genomes to determine the microbial information corresponding to the sample object; the determination module is also used to determine the number of unique molecular identifiers (UMIs) of the host gene corresponding to each spatial sampling site based on the payload sequences that successfully match the preset host reference genome; and to filter the microbial information corresponding to the sample object based on the type of microorganism contained in each spatial sampling site, the number of unique molecular identifiers (UMIs) corresponding to each microbial type, and the number of unique molecular identifiers (UMIs) of the host gene corresponding to each spatial sampling site. The mining module is used to determine the microbial information that differs between the sample object and the normal object based on the microbial information corresponding to the sample object and the microbial information corresponding to the normal object.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for microbial information mining of spatial transcriptome data as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for microbial information mining of spatial transcriptome data as described in any one of claims 1 to 5.

9. A computer program product having executable instructions stored thereon, characterized in that, When executed by the processor, this instruction causes the processor to implement the method for microbial information mining of spatial transcriptome data as described in any one of claims 1 to 5.

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