A multi-to-multi drug screening method for CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model and its applications.

Abstract

This invention provides a multi-to-multi drug screening method for CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model and its applications, belonging to the field of biomedical technology. The method includes the following steps: Step (1): Constructing a screening cell pool; Step (2): Classifying the drugs to be screened; Step (3): Constructing a drug hybrid screening model; Step (4): Grouping drugs according to mutually perpendicular X, Y, and Z planes, with drugs in each plane mixed to form independent pools; Step (5): Processing each pool into a cell pool; Step (6): Detecting and analyzing the changes in sgRNA abundance in each pool; Step (7): Decoding to determine the drug-GPCR interaction pairs. This invention offers higher screening throughput, realizes a multi-target, multi-drug screening mode, reduces costs and time, and enables large-scale research on drug-gene interaction networks, which is of great significance for drug research and development.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a multi-to-multi drug screening method for CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model and its applications. Background Technology

[0002] CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi) technologies (i.e., CRISPRa / i technology) are gene regulation tools derived from the CRISPR-Cas9 system. By fusing an inactivated Cas9 (dCas9) protein with transcriptional activation / repression effector domains (such as VP64, p65AD, KRAB, etc.), the expression of specific genes can be precisely upregulated or downregulated without DNA cleavage. This technology has advantages such as high throughput, high specificity, low off-target effects, and reversible regulation, making it ideal for loss-of-function and gain-of-function screening of functional genes.

[0003] G protein-coupled receptors (GPCRs) are the largest family of membrane proteins in the human genome and are targets for over 30% of modern drugs. They play crucial roles in tumorigenesis, development, immune evasion, and metastasis, representing a highly promising repertoire of anti-tumor drug targets. Developing more drugs targeting GPCRs is of great significance for clinical medical students.

[0004] CRISPRa / i high-throughput screening utilizes CRISPRa / i technology as a gene regulation tool, combined with library construction, drug processing, and NGS sequencing, to perform large-scale, systematic gene function screening and drug-target discovery. In CRISPRa / i high-throughput screening, "mixed drug screening" refers to processing and analyzing multiple drugs together in the same culture system, which can achieve screening at a lower cost and with less labor. Its core technological challenge lies in how to decode gRNA sequences using next-generation sequencing (NGS) to attribute specific phenotypes (such as survival or death) to drug perturbations. CRISPRa / i-based drug screening holds promise for developing more drugs targeting GPCRs.

[0005] In the existing technology, the drug screening technology based on CRISPRa / i mainly includes the following steps: (1) Establishing a cell line that stably expresses the CRISPRa / i system: using lentivirus to introduce the dCas9-VPR (activation) or dCas9-KRAB (inhibition) system into target cells (such as HEK293T, NIH / 3T3, B16, etc.) to establish a stable expression line; (2) sgRNA design and transduction: designing efficient and low off-target sgRNA sequences according to the target gene, referring to CRISPRa or Dolcetto / Calabrese libraries; using lentivirus to transduce sgRNA to establish a cell line that stably expresses sgRNA, ensuring that each cell only expresses sgRNA. Contains one sgRNA (MOI<0.7); CRISPRa / i-sgRNA stable cell lines are treated with drugs or drug combinations (including mixed drug models), and different concentration gradients and time points can be set to evaluate the effects of drugs on functional phenotypes such as cell proliferation, apoptosis, and differentiation; (3) Drug treatment and screening: CRISPRa / i-sgRNA stable cell lines are treated with drugs, and a single drug is added to a single library to evaluate which specific genes the drug affects, thereby promoting functional phenotypes such as cell proliferation, apoptosis, and differentiation; (4) Data analysis: After NGS sequencing, the treated group is compared with the control group without drug treatment, and key regulatory genes are identified by sgRNA abundance analysis using statistical methods (such as MAGeCK algorithm).

[0006] The existing technologies described above have the following inherent drawbacks and limitations: limited screening dimensions and throughput: Most existing technologies operate on a "one-to-many" model, meaning that a single screening can only test the effect of one drug on a single gene perturbation library. To test multiple drugs, multiple independent screening experiments are required, which are costly, time-consuming, and cumbersome, failing to meet the research needs of large-scale drug combinations or drug-gene interaction networks. This is essentially a "low-dimensional" screening. The framework of existing technologies limits their ability to expand to screening for higher throughput and more complex interaction relationships. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for many-to-many drug screening of CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model and its applications.

[0008] This invention relates to the interdisciplinary field of high-throughput drug screening, functional genomics, and cell biology techniques. Specifically, it relates to a method for discovering novel antitumor drug targets and candidate compounds by utilizing activating / inhibiting CRISPR (CRISPRa / i) technology, targeting G protein-coupled receptor (GPCR) libraries, and performing hybrid, many-to-many high-throughput screening in a three-dimensional hybrid drug model.

[0009] This invention provides a method for many-to-many drug screening of CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model, comprising the following steps:

[0010] Step (1): Construct a cell pool for screening CRISPR activation and / or CRISPR interference GPCR gene libraries;

[0011] Step (2): Classify the drugs to be screened;

[0012] Step (3): Construct a drug hybrid screening model based on a three-dimensional spatial coordinate system, and assign each drug a unique three-dimensional coordinate (x, y, z), where x, y, and z represent the position of the drug in the three dimensions, respectively;

[0013] Step (4): Group the drugs in the three-dimensional space according to the mutually perpendicular X-plane, Y-plane and Z-plane, and mix the drugs in each plane to form independent mixing pools, so that each drug appears once in each of the three different mixing pools;

[0014] Step (5): Treat the CRISPRa and / or CRISPRi cell pools separately in each pool for drug screening;

[0015] Step (6): Next-generation sequencing was used to detect changes in the abundance of sgRNA in each pool and differential gene expression analysis was performed;

[0016] Step (7): Decode and identify drug-GPCR action pairs by finding common GPCR changes in the three orthogonal plane data.

[0017] Furthermore, in step (1), the method for constructing a CRISPR activation and / or CRISPR interference GPCR gene library screening cell pool includes the following steps:

[0018] (a) Using dCas9-VP64 fusion protein and / or dCas9-KRAB fusion protein, respectively, lentiviruses were transduced into host cells to establish cell lines that stably express dCas9-VP64 and / or stably express dCas9-KRAB.

[0019] (b) Design CRISPRa sgRNA libraries and / or CRISPRi sgRNA libraries targeting the GPCR gene family;

[0020] (c) The CRISPRa sgRNA library was transduced into a cell line stably expressing dCas9-VP64 via lentivirus, and the CRISPRi sgRNA library was transduced into a cell line stably expressing dCas9-KRAB via lentivirus.

[0021] (d) Cells that successfully integrated sgRNA were screened to obtain CRISPRa and CRISPRi cell pools, respectively.

[0022] Preferably, in step (c), when transducing sgRNA, the multiplicity of infection (MOI) is controlled to be approximately 0.3.

[0023] Preferably, in step (d), the screening method is to use puromycin.

[0024] Furthermore, in step (2), classifying the drugs to be screened includes using one type of compound each time, with the same compound concentration and solvent.

[0025] Preferably, in step (2), the compound is an alkaloid, terpene, flavonoid, phenylpropanoid or other type.

[0026] Preferably, in step (2), the concentration of the compound is 100 mM or 10 mM;

[0027] Preferably, in step (2), the solvent is DMSO, alcohol, or water.

[0028] Furthermore, in step (3), the three-dimensional spatial hybrid screening model is an n×n×n three-dimensional spatial structure, where n is an integer greater than or equal to 2, and the total drug carrying capacity is n. 3 This forms 3n two-dimensional planar mixing cells, each containing n... 2 One drug.

[0029] Furthermore, in step (3), the three-dimensional spatial hybrid screening model is a 6×6×6 structure, carrying 216 drugs and forming 18 mixing pools, each containing 36 drugs.

[0030] Further, in step (4), the preparation of the mixing tank includes: taking equal volumes of each drug corresponding to the plane from the mother liquor plate and mixing them, diluting them to the working concentration using complete culture medium, and controlling the final drug concentration to be 10 μM.

[0031] Further, in step (5), the treatment conditions are as follows: each mixed cell pool is added to each cell pool and cultured for 24-72 hours.

[0032] And / or, in step (6), the screening criteria for the differential gene expression analysis are |mean logFC|>1 and P<0.05.

[0033] Further, in step (7), the step of decoding to determine the drug-GPCR interaction pair includes:

[0034] (A) Statistically analyze the GPCR extinction and enrichment in the X, Y, and Z planes respectively;

[0035] (B) Screening for GPCRs that show significant changes in all three orthogonal planes;

[0036] (C) Determine the unique intersection point of the three planes and assign it to a specific drug;

[0037] (D) This leads to the identification of drugs that cause specific GPCR changes and the establishment of drug-GPCR interaction pairs.

[0038] Furthermore, the method is used to screen GPCR agonists or antagonists, including positive screening and negative screening, wherein:

[0039] CRISPRa library screening is used to identify GPCR agonists; positive screening corresponds to GPCR activation, and negative screening corresponds to GPCR inhibition.

[0040] CRISPRi library screening is used to identify GPCR antagonists; positive screening corresponds to GPCR inhibition, and negative screening corresponds to GPCR activation.

[0041] This invention also provides the use of the aforementioned method in drug target discovery, GPCR function studies, or antitumor drug screening.

[0042] Existing drug screening technologies based on CRISPRa / i can only apply one drug treatment in the same system, thus making it impossible to simultaneously test the effects of multiple different antitumor drugs (or compound combinations) on a mixed gene-perturbed cell library in the same experimental system. However, the three-dimensional spatial hybrid screening model constructed in this invention can achieve true "many-to-many" parallel screening.

[0043] This invention provides a method and system for many-to-many drug screening of CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model. The CRISPRa / i cell library screening method and system have higher throughput and realize a "many-to-many" multi-target multi-drug screening mode. A single screening can achieve the effects of multiple drugs on a single gene library, reducing costs and time, and enabling large-scale research on drug-gene interaction networks. This is of great significance for drug research and development.

[0044] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0045] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0046] Figure 1 This is a complete experimental flowchart of the CRISPRa / i GPCR library many-to-many drug screening method based on a three-dimensional spatial hybrid screening model, as described in this invention.

[0047] Figure 2 This is a map showing the drug distribution information for each plane of a three-dimensional mixing pool model.

[0048] Figure 3 Figure 1 shows the results of CCK-8 assay combined with flow cytometry to detect the effects of different drug-GPCR combinations on the proliferation and apoptosis of A549 cells: A is the result of the 2F7-P2RY1 combination; B is the result of the 3A12-PTGIR combination; C is the result of the 1B3-PTH2R combination; D is the result of the 1B12-S1PR5 combination; E is the result of the 3A8-NPY5R combination; F is the result of the 2G10-ADRA2B combination; G is the result of the 1A5 / 1B5-H combination. CAR3 combination result diagram; H is the 1C2 / 1C4-ADRA2C combination result diagram; I is the 1E2 / 1E11 / 1F10-SSTR4 combination result diagram; J is the 1G8 / 1G9 / 2G12-GPR20 combination result diagram; K is the 1B2 / 1H2 / 2C8-GPR27 combination result diagram; L is the 1C3 / 2E5 / 2F3 / 2H3-GRM7 combination result diagram; M is the 2B4 / 2B12 / 2H6-SSTR5 combination result diagram. Detailed Implementation

[0049] Unless otherwise specified, the raw materials and equipment used in the specific embodiments of the present invention are all known products and were obtained by purchasing commercially available products.

[0050] To process large numbers of drug-gene combinations in a short time, improve screening efficiency, reduce screening time and cost, and achieve many-to-many screening, this invention establishes a three-dimensional spatial mixing pool model for CRISPRa / i cell libraries. The technical solution of this invention is illustrated below using a specific example.

[0051] Example 1: A Many-to-Many Drug Screening Method for CRISPRa / i GPCR Libraries Based on a Three-Dimensional Spatial Hybrid Screening Model

[0052] The flowchart of the many-to-many drug screening process for CRISPRa / i GPCR libraries based on the three-dimensional spatial hybrid screening model of this invention is as follows: Figure 1As shown, the specific filtering method is as follows:

[0053] 1. Construction of CRISPRa / i GPCR library

[0054] 1.1 Cell lines and vector systems

[0055] Human lung adenocarcinoma cell line A549 was selected as the screening cell model. Two screening systems, CRISPR activation (CRISPRa) and CRISPR interference (CRISPRi), were constructed respectively.

[0056] CRISPRa system: Using the dCas9-VP64 fusion protein, A549 cell lines stably expressing dCas9-VP64 were established by transducing the protein into A549 cells via lentivirus.

[0057] CRISPRi system: Using the dCas9-KRAB fusion protein, A549 cell lines stably expressing dCas9-KRAB were established by transducing the protein into A549 cells via lentivirus.

[0058] 1.2 sgRNA library design and transduction

[0059] Designing sgRNA libraries targeting the GPCR gene family:

[0060] (1) Determination of the target gene set

[0061] Based on the NCBI RefSeq or Ensembl database, obtain a list of all annotated human GPCR genes. For each gene, select authoritative major transcripts (such as canonical isoforms with the NM prefix in RefSeq) as design templates.

[0062] (2) sgRNA design principles

[0063] CRISPRa sgRNA targets the promoter region within a range of -400 bp upstream to +1 bp downstream of the transcription start site (TSS) to activate endogenous gene expression. When designing the RNA, sequences close to the TSS are preferred, and repetitive sequences or low-complexity regions should be avoided.

[0064] CRISPRi sgRNA targets the first exon or early coding region within the range of +50 bp to +300 bp downstream of the TSS of the gene to effectively inhibit transcriptional elongation. It also avoids splicing sites and repetitive sequences.

[0065] (3) Design tools and algorithms

[0066] Use a validated sgRNA design tool, such as CRISPick (Broad Institute, https: / / portals.broadinstitute.org / gppx / crispick / public) or CRISPOR (http: / / crispor.tefor.net / ), and set the following parameters:

[0067] Reference genome: GRCh38 / hg38.

[0068] PAM sequence: NGG for SpCas9 (for CRISPRa / i, both of which are SpCas9 systems).

[0069] sgRNA length: 20 nt + NGG (i.e., 20 bp target sequence followed by PAM).

[0070] Off-target scoring: Specificity scores (such as MIT specificity score or CFD specificity score) are used for filtering, and sgRNAs with fewer off-target sites and higher scores are given priority.

[0071] Efficiency prediction: For CRISPRa, enable the efficiency prediction model based on sequence features (such as the Doench '16 score); for CRISPRi, refer to the optimized model for suppression in CRISPick.

[0072] (4) sgRNA screening criteria

[0073] For each gene, 3-5 high-scoring sgRNAs were ultimately selected, with the specific selection criteria as follows:

[0074] Uniqueness: The sgRNA target sequence has only one perfect match in the human genome (the number of off-target sites with ≤1 mismatch is allowed below the threshold, for example, the number of off-target sites with CFD score <0.2 is no more than 10).

[0075] GC content: 30%-80%, to avoid extreme GC content affecting sgRNA stability and efficiency.

[0076] Continuous T bases: ≤4 consecutive T bases to prevent premature termination of transcription driven by the U6 promoter.

[0077] Location preference: CRISPRa's sgRNA is as close as possible to the TSS (e.g., within ±100 bp of the TSS), while CRISPRi's sgRNA is as close as possible to the transcription start site downstream of 50-150 bp.

[0078] Avoid SNPs and repetitive regions: exclude sgRNAs that target common SNP sites (MAF>0.01) or repetitive sequences (such as ALU, LINE, etc.).

[0079] CRISPRa sgRNA library: contains 2581 sgRNA constructs targeting the human GPCR gene family;

[0080] CRISPRi sgRNA library: contains 2556 sgRNA constructs targeting the human GPCR gene family.

[0081] The sgRNA library was transduced into the two stable cell lines mentioned above using lentiviruses, with the multiplicity of infection (MOI) controlled at approximately 0.3 to ensure that each cell integrates an average of about 0.3 sgRNAs, thereby ensuring that most cells carry only a single sgRNA.

[0082] 1.3 Cell Screening and Amplification

[0083] After transduction, the cells were treated as follows:

[0084] (1) Puromycin screening: After transduction and culture for 7 days, cells that successfully integrated sgRNA were screened using puromycin;

[0085] (2) Cell pool construction: CRISPRa cell pool and CRISPRi cell pool were obtained respectively; the corresponding cells of each cell pool were mixed together;

[0086] (3) Cell grouping: Each cell pool was divided into a control group (Vehicle treatment) and a drug treatment group (Drug treatment); the control group was the same cell pool as the drug treatment group.

[0087] 2. Construction of a three-dimensional spatial hybrid screening model

[0088] 2.1 Compound Classification and Pretreatment

[0089] Compounds are categorized based on drug type, parent concentration, and solvent to minimize false positives caused by potential drug synergies. In the screening system of this invention, drugs are classified into five types: alkaloids, terpenes, flavonoids, phenylpropanoids, and others. Parent concentrations are categorized into 100 mM and 10 mM, and solvents are categorized into DMSO, alcohol, and water. Each mixed screening model includes one drug type, one defined parent concentration, and one defined solvent.

[0090] In this embodiment, 216 terpenoid compounds (100 mM mother liquor, dissolved in DMSO) were selected and dispensed into 96-well plates for later use.

[0091] 2.2 Assignment of three-dimensional spatial coordinates

[0092] A 6×6×6 three-dimensional hybrid screening model with 6 X-planes, 6 Y-planes, and 6 Z-planes was constructed. This model can accommodate 216 drugs (6 3 =216). Each drug is assigned a unique three-dimensional coordinate (x, y, z), where x, y, z ∈ {0, 1, 2, 3, 4, 5}.

[0093] In three-dimensional space, each coordinate point represents a specific drug. For example, coordinate (0, 0, 0) represents drug 1-A1, coordinate (0, 0, 1) represents drug 1-A2, and so on.

[0094] 2.3 Dimensionality Reduction Mapping from 3D to 2D

[0095] In the 6×6×6 three-dimensional spatial model, each drug appears once in each of three mutually perpendicular planes:

[0096] X-plane (YZ-plane): The X-coordinate is fixed, while the Y and Z coordinates vary, resulting in a total of 6 planes;

[0097] Y-plane (XZ-plane): The Y-coordinate is fixed, while the X and Z coordinates vary, resulting in a total of 6 planes;

[0098] Z-plane (XY-plane): The Z-coordinate is fixed, while the X and Y coordinates vary, resulting in a total of 6 planes.

[0099] Each plane contains 36 drugs (6×6), forming 36 coordinate points.

[0100] 3. Mixed tank preparation and drug treatment

[0101] 3.1 Preparation of Mixed Cell

[0102] The 18 planes in three-dimensional space (6 X-planes + 6 Y-planes + 6 Z-planes) are used as independent mixing pools, as shown in Table 1:

[0103] Table 1. Numbering of the 18 mixing cells in three-dimensional space

[0104]

[0105] Figure 2 This is a drug distribution information map for each plane of the three-dimensional spatial mixing pool model; each plane mixes 36 drugs, and each drug is represented by a code, such as 1-A1.

[0106] Preparation method of each mixing cell:

[0107] Take 1 μL of each of the 36 drug samples corresponding to the plane in the 96-well plate (drug samples are taken according to the coordinate points);

[0108] Mix the 36 drugs thoroughly in centrifuge tubes;

[0109] Dilute to working concentration using complete culture medium, controlling the final concentration to 10 μM.

[0110] 3.2 Drug treatment

[0111] The 18 mixed pools were used to process the CRISPRa and CRISPRi cell pools respectively:

[0112] (1) Arrange the cells at an appropriate density (2×10⁻⁶) 6 Inoculate into a culture plate;

[0113] (2) Add 18 different mixed-cell drugs to each mixed-cell treatment, with each mixed-cell treatment treating an independent cell population;

[0114] (3) A control group (with only bulk solvent loaded) was set up at the same time;

[0115] (4) Collect cells 48 hours after drug treatment.

[0116] 4. NGS Sequencing and Data Analysis

[0117] 4.1 Sample Preparation and Sequencing

[0118] 48 hours after drug treatment:

[0119] (1) Collect cells from each group and extract genomic DNA;

[0120] (2) PCR amplification of the sgRNA sequence;

[0121] (3) Perform next-generation sequencing (NGS) to obtain the read count of sgRNA in each sample.

[0122] 4.2 Differential Gene Expression Analysis

[0123] Differential gene expression (DGE) analysis was performed on the drug-loaded group and drug group in the CRISPRa / i cell library using the screening criteria of |mean logFC|>1 and P<0.05.

[0124] 4.3 Three-dimensional decoding and target identification

[0125] The gene perturbation patterns in each plane (two-dimensional space) are statistically analyzed, and the drug-GPCR action pairs are determined through dimensionality reduction decoding from three dimensions to two dimensions.

[0126] Decoding principle: If a specific drug has biological activity, it will cause the same GPCR changes in three orthogonal planes (X-plane, Y-plane, and Z-plane). By finding the intersection of the data in the three planes, the drug that causes the specific GPCR change can be uniquely identified.

[0127] Detailed decoding steps:

[0128] Taking the data analysis of the X0, Y0, and Z0 planes as examples, this invention statistically analyzed the GPCR extinction and enrichment in these three planes respectively:

[0129] X0 plane analysis results:

[0130] Significantly eliminated GPCRs: ADDRD2, HTR1E, CXCR4, GPR132, etc. (20 in total)

[0131] Significantly enriched GPCRs: 20 including CCKBR, GPR34, ADGRE5, and GPR6.

[0132] Y0 plane analysis results:

[0133] Significantly extinct GPCRs: 10 including TACR2, MRGPRX4, FZD6, and GPR132

[0134] Significantly enriched GPCRs: 14 including NPBWR1, FFAR4, SSTR4, and PTH2R.

[0135] Z0 plane analysis results:

[0136] Significantly extinct GPCRs: HCRTR1, GPR132, TAS2R19, OPN1SW, etc. (16 in total)

[0137] Significantly enriched GPCRs: 15 including VN1R3, LGR5, NPBWR1, and ADGRF4.

[0138] Intersection analysis:

[0139] GPCRs that die out in three planes: GPR132

[0140] The unique intersection point of the three planes X0, Y0, and Z0 is (0, 0, 0).

[0141] Corresponding drug: 1-A1

[0142] Therefore, it was determined that drug 1-A1 was the cause of the significant elimination of GPR132.

[0143] 5. Screening Results and Validation

[0144] 5.1 Overall Screening Results

[0145] Using the above-mentioned 6×6×6 three-dimensional spatial hybrid screening model, and following the same method, in the CRISPRa library: (1) positive screening identified 80 compound-GPCR pairs, involving 14 GPCRs and 68 compounds; (2) negative screening identified 90 compound-GPCR pairs, involving 21 GPCRs and 84 compounds. In the CRISPRi library: (1) positive screening detected 2803 compound-GPCR pairs, involving 93 GPCRs and 582 compounds; (2) negative screening detected 731 compound-GPCR pairs, involving 73 GPCRs and 279 compounds. The screening results are as follows: Figure 3 As shown.

[0146] 5.2 Experimental Verification

[0147] Twenty-six compound-GPCR pairs were selected for independent experimental validation:

[0148] A549 cells were treated with a single compound;

[0149] Detect phenotypic changes such as cell proliferation and apoptosis;

[0150] Validation results: All 26 compound-GPCR pairs showed inhibitory effects on A549 cells, with a screening accuracy of 100%.

[0151] 6. Verification of Technological Advantages

[0152] The comparison of screening throughput between the three-dimensional spatial hybrid screening of the present invention and the traditional "one-to-many" screening is shown in Table 2.

[0153] Table 2. Comparison of screening throughput between the three-dimensional spatial hybrid screening of the present invention and the traditional "one-to-many" screening.

[0154]

[0155] As shown in Table 2, the screening throughput of the screening method of the present invention is significantly improved.

[0156] This invention provides three biological replicates by (1) triple independent detection: each drug appears once in the X, Y and Z planes; (2) statistical intersection: only when the same GPCR change is shown in all three planes is it judged as positive, effectively excluding false positives; (3) systematic classification strategy: classify according to drug type, concentration and solvent to reduce synergistic interference and ensure the accuracy of the results.

[0157] In summary, this invention provides a many-to-many drug screening method for CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model. This invention, based on CRISPRa / i cell libraries, has higher screening throughput and realizes a "many-to-many" multi-target multi-drug screening mode. A single screening can achieve the effects of multiple drugs on a single gene library, reducing costs and time, and enabling large-scale research on drug-gene interaction networks. This is of great significance for drug research and development.

Claims

1. A method for many-to-many drug screening of CRISPRa / i GPCR libraries based on a three-dimensional spatial hybrid screening model, characterized in that: Includes the following steps: Step (1): Constructing a cell pool for screening CRISPR activation and CRISPR interference GPCR gene libraries; Step (2): Classify the drugs to be screened; Step (3): Construct a drug hybrid screening model based on a three-dimensional spatial coordinate system, and assign each drug a unique three-dimensional coordinate (x, y, z), where x, y, and z represent the position of the drug in the three dimensions, respectively; Step (4): Group the drugs in the three-dimensional space according to the mutually perpendicular X-plane, Y-plane and Z-plane. Mix the drugs in each plane to form independent mixing pools, so that each drug appears once in each of the three different mixing pools. Step (5): The CRISPRa and CRISPRi cell pools were treated separately for drug screening; Step (6): Next-generation sequencing was used to detect changes in the abundance of sgRNA in each pool and differential gene expression analysis was performed; Step (7): Decode and determine the drug-GPCR interaction pair by finding common GPCR changes in the three orthogonal plane data; The step of decoding to determine the drug-GPCR interaction pair includes: (A) Statistically analyze the GPCR extinction and enrichment in the X, Y, and Z planes respectively; (B) Screening for GPCRs that show significant changes in all three orthogonal planes; (C) Determine the unique intersection point of the three planes and assign it to a specific drug; (D) This leads to the identification of drugs that cause specific GPCR changes and the establishment of drug-GPCR interaction pairs.

2. The method according to claim 1, characterized in that: In step (1), the method for constructing the CRISPR activation and CRISPR interference GPCR gene library screening cell pool includes the following steps: (a) Using dCas9-VP64 fusion protein and dCas9-KRAB fusion protein, cell lines stably expressing dCas9-VP64 and dCas9-KRAB were established by transducing them into host cells via lentivirus, respectively. (b) Design CRISPRa sgRNA libraries and CRISPRi sgRNA libraries targeting the GPCR gene family; (c) The CRISPRa sgRNA library was transduced into a cell line stably expressing dCas9-VP64 via lentivirus, and the CRISPRi sgRNA library was transduced into a cell line stably expressing dCas9-KRAB via lentivirus. (d) Cells that successfully integrated sgRNA were screened to obtain CRISPRa and CRISPRi cell pools, respectively.

3. The method according to claim 1, characterized in that: In step (2), classifying the drugs to be screened includes using one type of compound each time, with the same compound concentration and solvent.

4. The method according to claim 1, characterized in that: In step (3), the three-dimensional spatial hybrid screening model is an n×n×n three-dimensional spatial structure, where n is an integer greater than or equal to 2, and the total drug carrying capacity is n. 3 This forms 3n two-dimensional planar mixing cells, each containing n... 2 One drug.

5. The method according to claim 4, characterized in that: In step (3), the three-dimensional spatial hybrid screening model has a 6×6×6 structure, carries 216 drugs, forms 18 mixing pools, and each mixing pool contains 36 drugs.

6. The method according to claim 1, characterized in that: In step (4), the preparation of the mixing tank includes: taking equal volumes of each drug corresponding to the plane from the mother liquor plate and mixing them, diluting them to the working concentration using complete culture medium, and controlling the final drug concentration to 10 μM.

7. The method according to claim 1, characterized in that: In step (5), the treatment conditions are as follows: each mixed cell pool is added to each cell pool and cultured for 24-72 hours. And / or, in step (6), the screening criteria for the differential gene expression analysis are |mean logFC| > 1 and P < 0.

05.

8. The method according to any one of claims 1-7, characterized in that: The method is used to screen GPCR agonists or antagonists, including positive screening and negative screening, wherein: CRISPRa library screening is used to identify GPCR agonists; positive screening corresponds to GPCR activation, and negative screening corresponds to GPCR inhibition. CRISPRi library screening is used to identify GPCR antagonists; positive screening corresponds to GPCR inhibition, and negative screening corresponds to GPCR activation.

9. Use of the method according to any one of claims 1-8 in drug target discovery, GPCR function study or antitumor drug screening.

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