A spatial multi-omics high-throughput robust encoding and probe implementation method

Abstract

The present application relates to the technical field of spatial multi-omics detection, in particular to a spatial multi-omics high-throughput robust encoding and probe implementation method, comprising the following steps: receiving spatial multi-omics sample detection requirements, performing spatial positioning and component protection preprocessing operations on the sample, eliminating the interference of sample impurities and component degradation on detection, and obtaining a spatial multi-omics sample to be detected with accurate positioning and complete components.

Description

Technical Field

[0001] This invention relates to the field of space multi-omics detection technology, specifically a high-throughput robust coding and probe implementation method for space multi-omics. Background Technology

[0002] Spatial multi-omics technology is a core technology for analyzing the spatial distribution characteristics of molecular components, intermolecular interactions, and regulatory mechanisms of the biological microenvironment in biological samples. It has irreplaceable application value in basic life science research, precision oncology, neurobiology, and clinical diagnosis. High throughput, high accuracy, and high robustness have become the core development trends of spatial multi-omics detection technology. The design of coding systems and the implementation of targeted probes are the core key technologies for high-throughput spatial multi-omics detection, and their performance directly determines the specificity, accuracy, spatial positioning precision, and high-throughput adaptability of the detection.

[0003] Existing spatial multi-omics coding and probe implementation techniques still suffer from numerous technical shortcomings, making it difficult to meet the practical application requirements of high-throughput detection. Firstly, coding systems often employ traditional linear weighting methods, failing to consider the unique spatial distribution characteristics, hierarchical features, and target abundance differences inherent in spatial multi-omics. This results in insufficient coding robustness, making them prone to coding crosstalk in high-throughput parallel detection scenarios, leading to a significant reduction in coding-decoding accuracy. Secondly, probe design suffers from poor matching with the coding system, using simplistic modification methods that fail to target the hierarchical information of the code. Furthermore, probes exhibit weak signal capture capabilities for low-abundance targets, exhibiting prominent non-specific binding phenomena and severe background signal interference. Thirdly, hybridization strategies lack hierarchical and differentiated design; during multi-probe parallel hybridization, the design is not tailored to the specific omics category. The system suffers from several shortcomings: First, the hybridization conditions of the coding features are not well adapted, resulting in poor spatiotemporal synchronization of the signal and a tendency for signal overlap and loss. Second, the decoding algorithms are mostly single-dimensional analytical modes with simple noise reduction methods and no matching verification at the coding level, leading to low accuracy in mapping the decoding results to the spatial location of the samples. Third, there is a lack of standardized and multi-dimensional verification mechanisms for the coding and probe systems. Once the parameters are fixed, there is no dynamic iterative optimization strategy, resulting in poor adaptability to samples with different tissue types and target abundances. Fourth, the supporting engines for coding design and probe synthesis are not specifically optimized for the spatial multi-omics domain, resulting in low processing efficiency in high-throughput scenarios. Furthermore, rule adjustments require a system restart, making rapid adaptation impossible and hindering the high-throughput detection needs of tens of thousands of targets.

[0004] In summary, a spatial multi-omics high-throughput robust coding and probe implementation method needs to be proposed to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a high-throughput robust coding and probe implementation method for spatial multi-omics, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention proposes a spatial multi-omics high-throughput robust coding and probe implementation method, comprising the following steps: S1. Receive spatial multi-omics sample detection requirements, perform spatial positioning and component protection preprocessing operations on the samples, eliminate the interference of sample impurities and component degradation on detection, and obtain spatial multi-omics samples to be detected with accurate positioning and complete components. S2. Based on the omics type, target quantity and spatial distribution characteristics of the sample to be tested, a multi-dimensional hierarchical robust coding system is designed to generate a unique coding set containing spatial location, omics type and target characteristics, so as to achieve the uniqueness and anti-interference design of the coding. S3. Based on the coding information of the robust coding set, the sequence design and specific modification of the target probe are carried out to achieve precise matching between the coding information and the probe structure and modification groups, and to synthesize a high-throughput specific probe set adapted to the coding system; S4. A hierarchical hybridization strategy is adopted to perform in situ hybridization between the probe set and the sample to be tested. The hybridization conditions are adapted by combining the encoded hierarchical features to achieve specific binding of the probe to the target site and spatiotemporal synchronous capture of the signal. S5. Based on the hierarchical rules of the coding system, a step-by-step decoding algorithm is used to robustly decode the captured hybrid signals, complete signal denoising and code matching verification, and generate multi-omics target decoding results with spatial positioning information; S6. Perform dual verification on the coding system and probe set, and sequentially verify the target recognition specificity, coding-decoding accuracy, and high-throughput detection accuracy. If the verification fails, optimize the coding design and probe modification scheme based on the verification results. If the verification passes, solidify the coding and probe system parameters. S7. Record key parameters and quality indicators of the entire process of coding design, probe synthesis and detection, and continuously optimize coding rules and probe implementation schemes based on sample detection feedback and verification results to achieve dynamic iteration of the technology system.

[0007] Preferably, the implementation process of step S1 is as follows: S1.1. Mark the spatial coordinates of the sample to be tested with a dot matrix to determine the spatial positioning reference of the sample; S1.2. The gradient paraformaldehyde fixation method is used to fix the samples in layers to achieve specific protection of nucleic acid, protein and metabolite components; S1.3. Cell debris and free nucleic acid impurities in the sample are removed by a combination of gradient centrifugation and enzymatic digestion. The integrity of the components of the processed sample is tested, and the sample that passes the test is determined to be a spatial multi-omics sample to be tested.

[0008] Preferably, the implementation process of step S2 is as follows: S2.1. Extract the omics type, target quantity, spatial distribution density, and target abundance characteristic parameters of the sample to be tested as input for coding design; S2.2. Construct a three-dimensional hierarchical coding structure of spatial location code, omics type code, and target feature code to generate basic coding sequences; S2.3. The basic coding sequence is optimized through two coding robustness coefficient calculation models. Model 1 is a spatially coupled coding robustness coefficient algorithm, as shown in equation (1): (1); Model 2 is a hierarchical dynamic adaptation coding robustness coefficient algorithm, as shown in equation (2): (2); In the formula, and The values ​​are all within the range of [0,1]. and The code was determined to be a valid code, among which The uniqueness score is determined by the encoding, and the value ranges from [0,1]. This is the uniqueness score performance weight, with values ​​ranging from [0.6, 1.2]. The Hamming distance normalization value is encoded using the range [0,1]. The Hamming distance performance weights are set to [0.4, 1.0]. The core feature adjustment factor has a value of 0.95. The value is [0,1], representing the matching degree between the encoding and the target features. The sample space distribution entropy takes values ​​in the range [0,1]. This is the spatial entropy decay factor, with a value of 0.1; This is the crosstalk suppression coefficient, with a value of 0.08; This is the encoding anti-crosstalk coefficient, with values ​​in the range [0,1]. For coding level identifier, Corresponding spatial location layer Corresponding omics type layer, Corresponding target feature layer, For the first The encoding feature score at each level, with values ​​[0,1]; For the first Dynamic weights of each level The sum is 1. For the first The logarithmic correction factor for the hierarchy, hour , hour , hour , For the first The feature gain value of the level, taking values ​​[0, 10]; This is a decoding difficulty correction factor, with a value of 0.05; The decoding difficulty coefficient has a value of [0,5]. S2.4. Perform crosstalk shielding on qualified codes to generate the final dedicated coding set, which includes coding sequence, hierarchical attributes, matching rules and spatial positioning mapping relationship.

[0009] Preferably, the implementation process of step S3 is as follows: S3.1. Based on the coding sequence and target features of the dedicated coding set, design the core binding region sequence of the probe to achieve accurate mapping between coding information and target binding features; S3.2. Modify the probe at both ends, with a positioning modification group matched with the spatial location code attached to the 5' end, and a signal reporter group matched with the omics type code attached to the 3' end; S3.3. Perform hierarchical coupling of signal amplification groups on probes corresponding to low-abundance targets, with the number of coupling groups inversely proportional to the target abundance; S3.4. The designed probes are synthesized and purified. Qualified probes are screened to form a high-throughput specific probe set by detecting sequence specificity and coupling efficiency of modified groups.

[0010] Preferably, the implementation process of step S4 is as follows: S4.1. Group the probe set according to the omics type code, and set the gradient hybridization temperature and hybridization time according to the nucleic acid, protein and metabolite omics types; S4.2. A layer-by-layer in situ hybridization method is adopted. First, probe hybridization with spatial location code matching is completed, and then probe hybridization with omics type code and target feature code matching is performed. A preliminary signal fixation is performed after each layer of hybridization is completed. S4.3. A high-resolution imaging system is used to capture signals from the hybridized samples. The capture resolution is matched with the positioning accuracy of the spatial location code to achieve spatiotemporal synchronous acquisition and storage of signals. S4.4. The sample is treated with a background signal masking reagent to eliminate the background signal generated by non-specific binding and retain the effective signal of specific binding between the probe and the target.

[0011] Preferably, the implementation process of step S5 is as follows: S5.1. Perform Gaussian filtering noise reduction on the captured valid signal and extract the signal's intensity, location, and wavelength characteristic parameters; S5.2. Decode step by step according to the hierarchical order of spatial location code, omics type code, and target feature code, and complete the matching of signal features and coding sequence in turn; S5.3. Perform encoding matching verification, compare the decoding result with the encoding rules of the dedicated encoding set. If the verification passes, it is determined to be a valid decoding result. If the verification fails, the signal features are extracted again for secondary decoding. S5.4. Map the effective decoding results to the spatial coordinate lattice markers of the samples to generate multi-omics target decoding results with precise spatial positioning information. The results include target type, abundance, spatial location and omics affiliation.

[0012] Preferably, the implementation process of step S6 is as follows: S6.1. Perform target recognition specificity verification, calculate the binding rate of the probe to non-target sites, and determine the verification as passed if the binding rate is ≤1%; S6.2. Verify the accuracy of encoding and decoding, and calculate the matching rate between the decoding results and the actual target information. A matching rate of ≥98% is considered a successful verification. S6.3. Perform high-throughput detection accuracy verification. Detect standard samples at a preset throughput and calculate the relative error of the detection results. If the relative error is ≤5%, the verification is considered passed. S6.4. Perform adaptability verification on samples with different tissue types and different target abundances. If the verification fails, optimize the coding robustness coefficient calculation parameters and probe modification scheme based on the error type. If the verification passes, solidify all design and synthesis parameters of the coding system and probe set.

[0013] Preferably, the implementation process of the robust optimization engine for coding design in step S2 is as follows: The basic encoding model with an encoding fault tolerance rate of ≥95% and a decoding matching speed of ≤500 milliseconds / line was selected as the core engine. The basic coding model was fine-tuned based on target data, sample type data, and spatial distribution data in the field of spatial multi-omics, with the goal of achieving a coding uniqueness score ≥0.9 and a coding-target feature matching degree ≥0.85. The engine performance is optimized by using coding compression and redundancy design techniques, achieving a single-sample coding design time of ≤10 minutes and supporting high-throughput coding design with tens of thousands of targets. Establish a collaborative mechanism between the main coding model and the dedicated omics coding model, improve coding robustness through the fusion of multi-model results, import feature dictionaries from various fields of spatial multi-omics, and regularly update the dictionary and model parameters based on detection feedback.

[0014] Preferably, the adaptation engine implementation process for probe synthesis and modification in step S3 is as follows: Configure the mapping rules between the encoding information and the probe sequence, modification group, and coupling site. The rules include triggering conditions, execution results, and priority levels 1-10. It has a built-in best practice library for probe synthesis, automatically applies design specifications for sequence specificity, modification site compatibility, and group coupling efficiency, and outputs optimization suggestions when conflicts occur; It can handle special cases of probe design such as low abundance targets, complex spatial microenvironments, and overlapping regions of multiple targets, and automatically adjust the probe sequence length and the type of modification group; It provides rule templates and a visual configuration wizard for probe design, supports hot reloading of rules and change logging, and rules take effect without restarting the engine after being updated.

[0015] Preferably, the implementation process of step S7 is as follows: Establish a record library of parameters and quality indicators for the entire process, including sample pretreatment parameters, coding design parameters, probe synthesis and modification parameters, hybridization and decoding parameters, and quality detection indicators for each step; Based on actual feedback data from sample testing, the changing trends of encoding / decoding accuracy, probe specificity, and detection precision were statistically analyzed. After every 20 batches of samples are tested, the calculation weights of the coding robustness coefficient and the coupling parameters of the probe modification group are calibrated, and the step-by-step decoding algorithm and the condition parameters of the hierarchical hybridization are optimized. The optimized parameters and rules are imported into the coding design robustness optimization engine and the probe synthesis modification and adaptation engine to achieve closed-loop iteration and performance improvement of the coding and probe technology system.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a multi-dimensional hierarchical robust coding system and employs a dual-coding robustness coefficient algorithm to optimize the coding sequence, thereby improving coding uniqueness, anti-crosstalk, and spatial adaptability, and solving the problem of high-throughput coding crosstalk. By precisely matching probes with coding information and performing hierarchical modification, it enhances the specificity of probe target recognition, strengthens the ability to capture low-abundance target signals, and reduces background signal interference. By implementing a hierarchical hybridization strategy and combining it with spatiotemporal synchronous signal capture, it improves the effectiveness and integrity of hybridization signals, avoiding signal overlap and loss. By using a step-by-step decoding algorithm combined with spatial coordinate mapping, it achieves accurate signal decoding, improving the spatial positioning accuracy of multi-omics targets. By establishing a triple verification mechanism and a closed-loop iterative strategy, coupled with a dedicated optimization engine, it ensures the reliability, flexible adaptability, and high-throughput processing capability of the detection system, achieving dynamic performance optimization of the technical system. The entire process forms a collaborative technical closed loop of coding-probe-detection, significantly improving the robustness, specificity, and accuracy of high-throughput detection of spatial multi-omics, providing core technical support for its large-scale application. Attached Figure Description

[0017] Figure 1 The present invention is shown. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] For examples, please refer to Figure 1 In practical applications, this invention proposes a spatial multi-omics high-throughput robust coding and probe implementation method, specifically including the following steps: S1. Receive spatial multi-omics sample detection requirements, perform spatial positioning and component protection preprocessing operations on the samples, eliminate the interference of sample impurities and component degradation on detection, and obtain spatial multi-omics samples to be detected with accurate positioning and complete components. In a specific embodiment, it should also be noted that the implementation process of step S1 is as follows: S1.1. Mark the spatial coordinates of the sample to be tested with a dot matrix to determine the spatial positioning reference of the sample; S1.2. The gradient paraformaldehyde fixation method is used to fix the samples in layers to achieve specific protection of nucleic acid, protein and metabolite components; S1.3. Cell debris and free nucleic acid impurities in the sample are removed by a combination of gradient centrifugation and enzymatic digestion. The integrity of the components of the processed sample is tested, and the sample that passes the test is determined to be a spatial multi-omics sample to be tested.

[0020] S2. Based on the omics type, target quantity and spatial distribution characteristics of the sample to be tested, a multi-dimensional hierarchical robust coding system is designed to generate a unique coding set containing spatial location, omics type and target characteristics, so as to achieve the uniqueness and anti-interference design of the coding. In a specific embodiment, it should also be noted that the implementation process of step S2 is as follows: S2.1. Extract the omics type, target quantity, spatial distribution density, and target abundance characteristic parameters of the sample to be tested as input for coding design; S2.2. Construct a three-dimensional hierarchical coding structure of spatial location code, omics type code, and target feature code to generate basic coding sequences; S2.3. The basic coding sequence is optimized through two coding robustness coefficient calculation models. Model 1 is a spatially coupled coding robustness coefficient algorithm, as shown in equation (1): (1); Model 2 is a hierarchical dynamic adaptation coding robustness coefficient algorithm, as shown in equation (2): (2); In the formula, and The values ​​are all within the range of [0,1]. and The code was determined to be a valid code, among which The uniqueness score is determined by the encoding, and the value ranges from [0,1]. This is the uniqueness score performance weight, with values ​​ranging from [0.6, 1.2]. The Hamming distance normalization value is encoded using the range [0,1]. The Hamming distance performance weights are set to [0.4, 1.0]. The core feature adjustment factor has a value of 0.95. The value is [0,1], representing the matching degree between the encoding and the target features. The sample space distribution entropy takes values ​​in the range [0,1]. This is the spatial entropy decay factor, with a value of 0.1; This is the crosstalk suppression coefficient, with a value of 0.08; This is the encoding anti-crosstalk coefficient, with values ​​in the range [0,1]. For coding level identifier, Corresponding spatial location layer Corresponding omics type layer, Corresponding target feature layer, For the first The encoding feature score at each level, with values ​​[0,1]; For the first Dynamic weights of each level The sum is 1. For the first The logarithmic correction factor for the hierarchy, hour , hour , hour , For the first The feature gain value of the level, taking values ​​[0, 10]; This is a decoding difficulty correction factor, with a value of 0.05; The decoding difficulty coefficient has a value of [0,5]. S2.4. Perform crosstalk shielding on qualified codes to generate the final dedicated coding set, which includes coding sequence, hierarchical attributes, matching rules and spatial positioning mapping relationship.

[0021] In a specific embodiment, it should also be noted that the implementation process of the robustness optimization engine for coding design in step S2 is as follows: The basic encoding model with an encoding fault tolerance rate of ≥95% and a decoding matching speed of ≤500 milliseconds / line was selected as the core engine. The basic coding model was fine-tuned based on target data, sample type data, and spatial distribution data in the field of spatial multi-omics, with the goal of achieving a coding uniqueness score ≥0.9 and a coding-target feature matching degree ≥0.85. The engine performance is optimized by using coding compression and redundancy design techniques, achieving a single-sample coding design time of ≤10 minutes and supporting high-throughput coding design with tens of thousands of targets. Establish a collaborative mechanism between the main coding model and the dedicated omics coding model, improve coding robustness through the fusion of multi-model results, import feature dictionaries from various fields of spatial multi-omics, and regularly update the dictionary and model parameters based on detection feedback.

[0022] S3. Based on the coding information of the robust coding set, the sequence design and specific modification of the target probe are carried out to achieve precise matching between the coding information and the probe structure and modification groups, and to synthesize a high-throughput specific probe set adapted to the coding system; In a specific embodiment, it should also be noted that the implementation process of step S3 is as follows: S3.1. Based on the coding sequence and target features of the dedicated coding set, design the core binding region sequence of the probe to achieve accurate mapping between coding information and target binding features; S3.2. Modify the probe at both ends, with a positioning modification group matched with the spatial location code attached to the 5' end, and a signal reporter group matched with the omics type code attached to the 3' end; S3.3. Perform hierarchical coupling of signal amplification groups on probes corresponding to low-abundance targets, with the number of coupling groups inversely proportional to the target abundance; S3.4. The designed probes are synthesized and purified. Qualified probes are screened to form a high-throughput specific probe set by detecting sequence specificity and coupling efficiency of modified groups.

[0023] In a specific embodiment, it should also be noted that the adaptation engine implementation process for probe synthesis and modification in step S3 is as follows: Configure the mapping rules between the encoding information and the probe sequence, modification group, and coupling site. The rules include triggering conditions, execution results, and priority levels 1-10. It has a built-in best practice library for probe synthesis, automatically applies design specifications for sequence specificity, modification site compatibility, and group coupling efficiency, and outputs optimization suggestions when conflicts occur; It can handle special cases of probe design such as low abundance targets, complex spatial microenvironments, and overlapping regions of multiple targets, and automatically adjust the probe sequence length and the type of modification group; It provides rule templates and a visual configuration wizard for probe design, supports hot reloading of rules and change logging, and rules take effect without restarting the engine after being updated.

[0024] S4. A hierarchical hybridization strategy is adopted to perform in situ hybridization between the probe set and the sample to be tested. The hybridization conditions are adapted by combining the encoded hierarchical features to achieve specific binding of the probe to the target site and spatiotemporal synchronous capture of the signal. In a specific embodiment, it should also be noted that the implementation process of step S4 is as follows: S4.1. Group the probe set according to the omics type code, and set the gradient hybridization temperature and hybridization time according to the nucleic acid, protein and metabolite omics types; S4.2. A layer-by-layer in situ hybridization method is adopted. First, probe hybridization with spatial location code matching is completed, and then probe hybridization with omics type code and target feature code matching is performed. A preliminary signal fixation is performed after each layer of hybridization is completed. S4.3. A high-resolution imaging system is used to capture signals from the hybridized samples. The capture resolution is matched with the positioning accuracy of the spatial location code to achieve spatiotemporal synchronous acquisition and storage of signals. S4.4. The sample is treated with a background signal masking reagent to eliminate the background signal generated by non-specific binding and retain the effective signal of specific binding between the probe and the target.

[0025] S5. Based on the hierarchical rules of the coding system, a step-by-step decoding algorithm is used to robustly decode the captured hybrid signals, complete signal denoising and code matching verification, and generate multi-omics target decoding results with spatial positioning information; In a specific embodiment, it should also be noted that the implementation process of step S5 is as follows: S5.1. Perform Gaussian filtering noise reduction on the captured valid signal and extract the signal's intensity, location, and wavelength characteristic parameters; S5.2. Decode step by step according to the hierarchical order of spatial location code, omics type code, and target feature code, and complete the matching of signal features and coding sequence in turn; S5.3. Perform encoding matching verification, compare the decoding result with the encoding rules of the dedicated encoding set. If the verification passes, it is determined to be a valid decoding result. If the verification fails, the signal features are extracted again for secondary decoding. S5.4. Map the effective decoding results to the spatial coordinate lattice markers of the samples to generate multi-omics target decoding results with precise spatial positioning information. The results include target type, abundance, spatial location and omics affiliation.

[0026] S6. Perform dual verification on the coding system and probe set, and sequentially verify the target recognition specificity, coding-decoding accuracy, and high-throughput detection accuracy. If the verification fails, optimize the coding design and probe modification scheme based on the verification results. If the verification passes, solidify the coding and probe system parameters. In a specific embodiment, it should also be noted that the implementation process of step S6 is as follows: S6.1. Perform target recognition specificity verification, calculate the binding rate of the probe to non-target sites, and determine the verification as passed if the binding rate is ≤1%; S6.2. Verify the accuracy of encoding and decoding, and calculate the matching rate between the decoding results and the actual target information. A matching rate of ≥98% is considered a successful verification. S6.3. Perform high-throughput detection accuracy verification. Detect standard samples at a preset throughput and calculate the relative error of the detection results. If the relative error is ≤5%, the verification is considered passed. S6.4. Perform adaptability verification on samples with different tissue types and different target abundances. If the verification fails, optimize the coding robustness coefficient calculation parameters and probe modification scheme based on the error type. If the verification passes, solidify all design and synthesis parameters of the coding system and probe set.

[0027] S7. Record key parameters and quality indicators of the entire process of coding design, probe synthesis and detection, and continuously optimize coding rules and probe implementation schemes based on sample detection feedback and verification results to achieve dynamic iteration of the technology system.

[0028] In a specific embodiment, it should also be noted that the implementation process of step S7 is as follows: Establish a record library of parameters and quality indicators for the entire process, including sample pretreatment parameters, coding design parameters, probe synthesis and modification parameters, hybridization and decoding parameters, and quality detection indicators for each step; Based on actual feedback data from sample testing, the changing trends of encoding / decoding accuracy, probe specificity, and detection precision were statistically analyzed. After every 20 batches of samples are tested, the calculation weights of the coding robustness coefficient and the coupling parameters of the probe modification group are calibrated, and the step-by-step decoding algorithm and the condition parameters of the hierarchical hybridization are optimized. The optimized parameters and rules are imported into the coding design robustness optimization engine and the probe synthesis modification and adaptation engine to achieve closed-loop iteration and performance improvement of the coding and probe technology system.

[0029] Through the above steps, this invention constructs a multi-dimensional hierarchical robust coding system, employs a dual-coding robustness coefficient algorithm to optimize the coding sequence, and improves coding uniqueness, anti-crosstalk, and spatial adaptability, thus solving the problem of high-throughput coding crosstalk. By precisely matching probes with coding information and performing hierarchical modification, it enhances the probe target recognition specificity, strengthens the ability to capture low-abundance target signals, and reduces background signal interference. By implementing a hierarchical hybridization strategy combined with spatiotemporal synchronous signal capture, it improves the effectiveness and integrity of hybridization signals, avoiding signal overlap and loss. Through a step-by-step decoding algorithm combined with spatial coordinate mapping, it achieves accurate signal decoding, improving the spatial positioning accuracy of multi-omics targets. By establishing a triple verification mechanism and a closed-loop iterative strategy, coupled with a dedicated optimization engine, it ensures the reliability, flexible adaptability, and high-throughput processing capability of the detection system, achieving dynamic performance optimization of the technical system. The entire process forms a collaborative technical closed loop of coding-probe-detection, significantly improving the robustness, specificity, and accuracy of high-throughput detection of spatial multi-omics, providing core technical support for its large-scale application.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A spatial multi-omics high-throughput robust coding and probe implementation method, characterized in that, Includes the following steps: S1. Receive spatial multi-omics sample detection requirements, perform spatial positioning and component protection preprocessing operations on the samples, eliminate the interference of sample impurities and component degradation on detection, and obtain spatial multi-omics samples to be detected with accurate positioning and complete components. S2. Based on the omics type, target quantity and spatial distribution characteristics of the sample to be tested, a multi-dimensional hierarchical robust coding system is designed to generate a unique coding set containing spatial location, omics type and target characteristics, so as to achieve the uniqueness and anti-interference design of the coding. S3. Based on the coding information of the robust coding set, the sequence design and specific modification of the target probe are carried out to achieve precise matching between the coding information and the probe structure and modification groups, and to synthesize a high-throughput specific probe set adapted to the coding system; S4. A hierarchical hybridization strategy is adopted to perform in situ hybridization between the probe set and the sample to be tested. The hybridization conditions are adapted by combining the encoded hierarchical features to achieve specific binding of the probe to the target site and spatiotemporal synchronous capture of the signal. S5. Based on the hierarchical rules of the coding system, a step-by-step decoding algorithm is used to robustly decode the captured hybrid signals, complete signal denoising and code matching verification, and generate multi-omics target decoding results with spatial positioning information; S6. Perform dual verification on the coding system and probe set, and sequentially verify the target recognition specificity, coding-decoding accuracy, and high-throughput detection accuracy. If the verification fails, optimize the coding design and probe modification scheme based on the verification results. If the verification passes, solidify the coding and probe system parameters. S7. Record key parameters and quality indicators of the entire process of coding design, probe synthesis and detection, and continuously optimize coding rules and probe implementation schemes based on sample detection feedback and verification results to achieve dynamic iteration of the technology system.

2. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 1, characterized in that, The implementation process of step S1 is as follows: S1.

1. Mark the spatial coordinates of the sample to be tested with a dot matrix to determine the spatial positioning reference of the sample; S1.

2. The gradient paraformaldehyde fixation method is used to fix the samples in layers to achieve specific protection of nucleic acid, protein and metabolite components; S1.

3. Cell debris and free nucleic acid impurities in the sample are removed by a combination of gradient centrifugation and enzymatic digestion. The integrity of the components of the processed sample is tested, and the sample that passes the test is determined to be a spatial multi-omics sample to be tested.

3. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 2, characterized in that, The implementation process of step S2 is as follows: S2.

1. Extract the omics type, target quantity, spatial distribution density, and target abundance characteristic parameters of the sample to be tested as input for coding design; S2.

2. Construct a three-dimensional hierarchical coding structure of spatial location code, omics type code, and target feature code to generate basic coding sequences; S2.

3. The basic coding sequence is optimized through two coding robustness coefficient calculation models. Model 1 is a spatially coupled coding robustness coefficient algorithm, as shown in equation (1): （1）； Model 2 is a hierarchical dynamic adaptation coding robustness coefficient algorithm, as shown in equation (2): （2）； In the formula, and The values ​​are all within the range of [0,1]. and The code was determined to be a valid code, among which The uniqueness score is determined by the encoding, and the value ranges from [0,1]. This is the uniqueness score performance weight, with values ​​ranging from [0.6, 1.2]. The Hamming distance normalization value is encoded using the range [0,1]. The Hamming distance performance weights are set to [0.4, 1.0]. The core feature adjustment factor has a value of 0.

95. The value is [0,1], representing the matching degree between the encoding and the target features. The sample space distribution entropy takes values ​​in the range [0,1]. This is the spatial entropy decay factor, with a value of 0.1; This is the crosstalk suppression coefficient, with a value of 0.08; This is the encoding anti-crosstalk coefficient, with values ​​in the range [0,1]. For coding level identifier, Corresponding spatial location layer Corresponding omics type layer, Corresponding target feature layer, For the first The encoding feature score at each level, with values ​​[0,1]; For the first Dynamic weights of each level The sum is 1. For the first The logarithmic correction factor for the hierarchy, hour , hour , hour , For the first The feature gain value of the level, taking values ​​[0, 10]; This is a decoding difficulty correction factor, with a value of 0.05; The decoding difficulty coefficient has a value of [0,5]. S2.

4. Perform crosstalk shielding on qualified codes to generate the final dedicated coding set, which includes coding sequence, hierarchical attributes, matching rules and spatial positioning mapping relationship.

4. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 3, characterized in that, The implementation process of step S3 is as follows: S3.

1. Based on the coding sequence and target features of the dedicated coding set, design the core binding region sequence of the probe to achieve accurate mapping between coding information and target binding features; S3.

2. Modify the probe at both ends, with a positioning modification group matched with the spatial location code attached to the 5' end, and a signal reporter group matched with the omics type code attached to the 3' end; S3.

3. Perform hierarchical coupling of signal amplification groups on probes corresponding to low-abundance targets, with the number of coupling groups inversely proportional to the target abundance; S3.

4. The designed probes are synthesized and purified. Qualified probes are screened to form a high-throughput specific probe set by detecting sequence specificity and coupling efficiency of modified groups.

5. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 4, characterized in that, The implementation process of step S4 is as follows: S4.

1. Group the probe set according to the omics type code, and set the gradient hybridization temperature and hybridization time according to the nucleic acid, protein and metabolite omics types; S4.

2. A layer-by-layer in situ hybridization method is adopted. First, probe hybridization with spatial location code matching is completed, and then probe hybridization with omics type code and target feature code matching is performed. A preliminary signal fixation is performed after each layer of hybridization is completed. S4.

3. A high-resolution imaging system is used to capture signals from the hybridized samples. The capture resolution is matched with the positioning accuracy of the spatial location code to achieve spatiotemporal synchronous acquisition and storage of signals. S4.

4. The sample is treated with a background signal masking reagent to eliminate the background signal generated by non-specific binding and retain the effective signal of specific binding between the probe and the target.

6. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 5, characterized in that, The implementation process of step S5 is as follows: S5.

1. Perform Gaussian filtering noise reduction on the captured valid signal and extract the signal's intensity, location, and wavelength characteristic parameters; S5.

2. Decode step by step according to the hierarchical order of spatial location code, omics type code, and target feature code, and complete the matching of signal features and coding sequence in turn; S5.

3. Perform encoding matching verification, compare the decoding result with the encoding rules of the dedicated encoding set. If the verification passes, it is determined to be a valid decoding result. If the verification fails, the signal features are extracted again for secondary decoding. S5.

4. Map the effective decoding results to the spatial coordinate lattice markers of the samples to generate multi-omics target decoding results with precise spatial positioning information. The results include target type, abundance, spatial location and omics affiliation.

7. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 6, characterized in that, The implementation process of step S6 is as follows: S6.

1. Perform target recognition specificity verification, calculate the binding rate of the probe to non-target sites, and determine the verification as passed if the binding rate is ≤1%; S6.

2. Verify the accuracy of encoding and decoding, and calculate the matching rate between the decoding results and the actual target information. A matching rate of ≥98% is considered a successful verification. S6.

3. Perform high-throughput detection accuracy verification. Detect standard samples at a preset throughput and calculate the relative error of the detection results. If the relative error is ≤5%, the verification is considered passed. S6.

4. Perform adaptability verification on samples with different tissue types and different target abundances. If the verification fails, optimize the coding robustness coefficient calculation parameters and probe modification scheme based on the error type. If the verification passes, solidify all design and synthesis parameters of the coding system and probe set.

8. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 7, characterized in that, The implementation process of the robust optimization engine in step S2 is as follows: The basic encoding model with an encoding fault tolerance rate of ≥95% and a decoding matching speed of ≤500 milliseconds / line was selected as the core engine. The basic coding model was fine-tuned based on target data, sample type data, and spatial distribution data in the field of spatial multi-omics, with the goal of achieving a coding uniqueness score ≥0.9 and a coding-target feature matching degree ≥0.

85. The engine performance is optimized by using coding compression and redundancy design techniques, achieving a single-sample coding design time of ≤10 minutes and supporting high-throughput coding design with tens of thousands of targets. Establish a collaborative mechanism between the main coding model and the dedicated omics coding model, improve coding robustness through the fusion of multi-model results, import feature dictionaries from various fields of spatial multi-omics, and regularly update the dictionary and model parameters based on detection feedback.

9. The spatial multi-omics high-throughput robust coding and probe implementation method according to claim 8, characterized in that, The adaptation engine implementation process for probe synthesis and modification in step S3 is as follows: Configure the mapping rules between the encoding information and the probe sequence, modification group, and coupling site. The rules include triggering conditions, execution results, and priority levels 1-10. It has a built-in best practice library for probe synthesis, automatically applies design specifications for sequence specificity, modification site compatibility, and group coupling efficiency, and outputs optimization suggestions when conflicts occur; It can handle special cases of probe design such as low abundance targets, complex spatial microenvironments, and overlapping regions of multiple targets, and automatically adjust the probe sequence length and the type of modification group; It provides rule templates and a visual configuration wizard for probe design, supports hot reloading of rules and change logging, and rules take effect without restarting the engine after being updated.

10. A spatial multi-omics high-throughput robust coding and probe implementation method according to claim 9, characterized in that, The implementation process of step S7 is as follows: Establish a record library of parameters and quality indicators for the entire process, including sample pretreatment parameters, coding design parameters, probe synthesis and modification parameters, hybridization and decoding parameters, and quality detection indicators for each step; Based on actual feedback data from sample testing, the changing trends of encoding / decoding accuracy, probe specificity, and detection precision were statistically analyzed. After every 20 batches of samples are tested, the calculation weights of the coding robustness coefficient and the coupling parameters of the probe modification group are calibrated, and the step-by-step decoding algorithm and the condition parameters of the hierarchical hybridization are optimized. The optimized parameters and rules are imported into the coding design robustness optimization engine and the probe synthesis modification and adaptation engine to achieve closed-loop iteration and performance improvement of the coding and probe technology system.

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