A lung cancer organoid chemotherapy drug resistance heterogeneity research method based on single cell sequencing

Abstract

The application discloses a lung cancer organoid chemotherapy drug resistance heterogeneity research method based on single cell sequencing, and comprises the following steps: S1, sterile separation and standardized culture of patient-derived lung cancer organoids; S2, gradient chemotherapy drug resistance induction modeling of lung cancer organoids; S3, preparation and sequencing library construction of drug resistance / sensitive lung cancer organoid single cell suspension; S4, single cell sequencing data preprocessing and cell subpopulation clustering analysis; S5, multidimensional chemotherapy drug resistance heterogeneity deep analysis; and S6, drug resistance core target point verification and heterogeneity mechanism confirmation. The application realizes drug resistance heterogeneity analysis at the single cell and single subpopulation levels, solves the problems that traditional technologies cannot distinguish cell subpopulations and mask core drug resistance characteristics, and realizes more accurate and more in-depth drug resistance mechanism analysis. A standardized organoid culture, drug resistance induction and sequencing analysis whole-process parameter system is established, experimental standards are unified, data differences caused by fragmentation of traditional experimental processes and parameter confusion are avoided, and the research result reliability is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of single-cell sequencing technology, specifically to a method for studying the heterogeneity of chemotherapy resistance in lung cancer organoids based on single-cell sequencing. Background Technology

[0002] Lung cancer is one of the malignant tumors with the highest incidence and mortality rates worldwide. Chemotherapy is the core clinical treatment for patients with intermediate and advanced lung cancer. However, the widespread occurrence of chemotherapy resistance is the core bottleneck leading to treatment failure, tumor recurrence and metastasis. Tumor heterogeneity is the fundamental cause of chemotherapy resistance in lung cancer. Differences in the proliferation, differentiation and drug response characteristics of different tumor cell subpopulations allow some cells to escape the killing effect of chemotherapy drugs and gradually form a stable drug-resistant phenotype.

[0003] Currently, research protocols for chemotherapy resistance in lung cancer in clinical and scientific research fields are mainly divided into two categories. One is the study of drug resistance mechanisms based on bulk sequencing of tumor tissue, which screens for chemotherapy resistance-related genes by detecting differences in overall gene expression and mutation profiles in tumor tissue. The other is the study of in vitro drug resistance models based on traditional tumor cell lines, which constructs drug-resistant cell lines through drug gradient induction and conducts verification of drug resistance mechanisms. In addition, in recent years, lung cancer organoid models have been gradually applied to drug resistance research. With their advantages of preserving the in situ biological characteristics of patient tumors, stable genetic background, and short culture cycle, they have made up for the shortcomings of cell line models with single genetic background, high cost and long cycle of animal models.

[0004] Current mainstream research technologies are insufficient to meet the needs of precise, single-cell-level research on drug resistance mechanisms in analyzing the heterogeneity of chemotherapy resistance in lung cancer. Bulk sequencing technology suffers from an averaging masking effect and cannot analyze cell subpopulation heterogeneity. Traditional batch sequencing detects the average gene expression of all cells in the tumor tissue, and cannot distinguish the expression differences of sensitive cells, drug-resistant cells, stromal cells, and immune cells. It is easy to mask the characteristic genes and abnormal pathways of rare drug-resistant cell subpopulations, making it difficult to discover the core cell subpopulations and precise targets that drive drug resistance. This results in low accuracy of current drug resistance target screening and poor clinical translation. Traditional drug resistance models lack heterogeneity and deviate significantly from real clinical drug resistance scenarios. Immortalized lung cancer cell lines undergo significant changes in genetic background, tumor stemness, and cell subpopulation structure after long-term in vitro passage culture, making it impossible to simulate the heterogeneous characteristics of tumors in patients. Traditional animal modeling methods are costly, have long experimental cycles, low throughput, and exhibit species differences, making it difficult to conduct large-scale research on the dynamic evolution of drug resistance. The system for studying organoid drug resistance is incomplete and lacks dynamic analysis methods at the single-cell level. Existing studies on lung cancer organoid drug resistance mostly use overall level drug efficacy testing and batch gene testing, which can only verify the overall drug resistance phenotype of organoids. They cannot track the dynamic evolution of different tumor cell subpopulations, the transformation of drug resistance phenotypes, and the process of gene expression remodeling under chemotherapy pressure, and cannot elucidate the generation mechanism and evolutionary law of drug resistance heterogeneity. The research process is fragmented, with poor standardization and reproducibility. Currently, there is no complete standardized process that integrates organoid precision culture, chemotherapy resistance induction, single-cell sequencing library construction, heterogeneity data analysis, and drug resistance target verification. The parameters of each experimental step are not uniform, and the comparability of data from different studies is low, which greatly limits the mechanism research and clinical translation of chemotherapy resistance heterogeneity in lung cancer. Summary of the Invention

[0005] To overcome the technical problems of existing lung cancer chemotherapy resistance heterogeneity research techniques, such as ambiguous cell subpopulation analysis, poor clinical relevance of models, fragmented research process, and inability to dynamically track drug resistance evolution, the present invention aims to provide a lung cancer organoid chemotherapy resistance heterogeneity research method based on single-cell sequencing.

[0006] To achieve the above objectives, this invention provides a method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing, the method comprising the following steps: S1: Aseptic isolation and standardized culture of lung cancer organoids from patients; S2: Modeling of resistance induction in lung cancer organoids under gradient chemotherapy; S3: Preparation of single-cell suspensions and sequencing library construction of drug-resistant / sensitive lung cancer organoids; S4: Preprocessing of single-cell sequencing data and analysis of cell subpopulations; S5: In-depth analysis of multidimensional chemotherapy resistance heterogeneity; S6: Validation of core resistance targets and confirmation of heterogeneity mechanisms.

[0007] In a preferred embodiment of the present invention, in step S1, surgical specimens or biopsy specimens from clinically diagnosed lung cancer patients are selected, and necrotic tissue, adipose tissue, and blood clots are removed under aseptic conditions. The tissue is then repeatedly rinsed 3-5 times with pre-cooled sterile PBS buffer and minced to 1 mm. 3 Small tissue blocks were digested with a complex digestion solution of collagenase IV and trypsin at 37°C with shaking for 30-60 minutes, with the cells being pipetted every 10 minutes. After digestion, the supernatant was removed by centrifugation, and the cells were filtered through a sterile filter to obtain a single-cell suspension. The cells were resuspended in a matrix gel and seeded into ultra-low adsorption culture plates. Complete culture medium specifically for lung cancer organoids was added, and the plates were incubated at 37°C in a 5% CO2 incubator. The culture medium was changed every 2-3 days, and the cells were cultured for 7-14 days until they aggregated into spherical organoids with a diameter of 50-200 μm. Primary lung cancer organoids with intact morphology and stable proliferation activity were selected, and apoptotic and malformed organoids were removed for later use.

[0008] In a preferred embodiment of the present invention, in step S2, first-line clinical chemotherapy drugs for lung cancer are selected, and a blank control group, low-concentration, medium-concentration, and high-concentration drug gradient groups are set up. Each group has 3 biological replicates. Mature lung cancer organoids are subjected to continuous drug gradient intervention culture for 7 to 21 days. During this period, the morphology and proliferation status of the organoids are dynamically observed. Cell activity is detected by CCK-8 assay and cell apoptosis rate is detected by flow cytometry to verify the drug resistance phenotype. Finally, a drug-resistant lung cancer organoid model with a stable chemotherapy resistance phenotype is obtained. At the same time, sensitive lung cancer organoids without drug intervention are retained as controls.

[0009] In a preferred embodiment of the present invention, in step S3, lung cancer organoids from the drug-resistant and drug-sensitive groups are taken respectively. After removing the matrix gel, single-cell dissociation is performed using a mild digestive enzyme. The digestion time and temperature are strictly controlled to avoid cell damage and apoptosis. After dissociation, a highly active, non-agglomerated single-cell suspension is obtained through centrifugation, filtration, and red blood cell lysis, ensuring a cell viability ≥90% and a cell concentration that meets sequencing standards. Library construction is performed based on the 10×Genomics single-cell RNA sequencing platform. Cell labeling, reverse transcription, cDNA amplification, library purification, and quality control are completed sequentially. The qualified library is subjected to high-throughput single-cell sequencing to obtain the raw single-cell transcriptome data of the two groups of organoids.

[0010] In a preferred embodiment of the present invention, in step S4, the raw sequencing data undergoes quality control, adapter removal, low-quality read removal, alignment with a reference genome, and quantitative gene expression analysis. Unqualified cells with excessive high or low expression of mitochondrial genes are removed. Data standardization and normalization are performed using Seurat software. Dimensionality reduction is achieved through PCA principal component analysis, and unsupervised cell clustering is performed using the t-SNE / UMAP algorithm. Cell type annotation is performed for each cell subpopulation based on lung cancer characteristic marker genes to distinguish between tumor stem cells, proliferating tumor cells, differentiated tumor cells, and stromal cells, accurately classifying the differences in cell subpopulation composition between the drug-resistant and drug-sensitive groups.

[0011] As a preferred embodiment of the present invention, in step S5, based on the results of single-cell grouping, drug resistance heterogeneity is analyzed from multiple dimensions at the cellular, gene, and pathway levels, including subgroup heterogeneity analysis, differential gene heterogeneity analysis, functional pathway heterogeneity analysis, cell trajectory evolution analysis, and cell communication heterogeneity analysis.

[0012] As a preferred embodiment of the present invention, the subpopulation heterogeneity analysis is to compare the differences in the proportion of each cell subpopulation between the drug-resistant group and the sensitive group, screen for rare drug-resistant cell subpopulations that significantly amplify and specifically enrich during the drug resistance process, and clarify the source of the core drug-resistant cells. The differential gene heterogeneity analysis targets specific drug-resistant cell subpopulations, screens for significantly differentially expressed genes between groups, identifies drug-resistant specific up- / down-regulated genes, and removes batch differences and background gene interference. The functional pathway heterogeneity analysis involves enriching differentially expressed genes using GO function, KEGG pathway, and GSEA gene set to identify abnormally activated drug resistance-related pathways such as tumor proliferation, anti-apoptosis, drug efflux, and DNA damage repair in drug-resistant cell subpopulations. The cell trajectory evolution analysis involves using pseudo-temporal analysis and RNA velocity analysis to track the dynamic evolution trajectory of tumor cells from a sensitive state to a drug-resistant state under chemotherapy stress, thereby clarifying the timing and evolutionary patterns of drug resistance heterogeneity. The cell communication heterogeneity analysis aims to analyze ligand-receptor interactions among different cell subpopulations in the drug-resistant microenvironment and reveal the regulatory mechanism of intercellular signaling on the formation of drug resistance heterogeneity.

[0013] In a preferred embodiment of the present invention, in step S6, key drug resistance genes specific to cell subpopulations and regulated by core pathways are screened as candidate targets. The mRNA and protein expression levels of the target genes are verified in drug-resistant organoid models using qRT-PCR and Western blot. The regulatory effects of the targets on tumor cell drug resistance, proliferation, and apoptosis are verified through cell function experiments. Combined with clinical lung cancer sample datasets, the correlation between the expression of candidate targets and the efficacy and prognosis of chemotherapy in patients is analyzed. Ultimately, the core molecular mechanisms and precise targets of chemotherapy resistance heterogeneity in lung cancer are identified.

[0014] The beneficial effects of this invention are: This invention enables the analysis of drug resistance heterogeneity at the single-cell and single-subpopulation levels, solving the problem that traditional techniques cannot distinguish cell subpopulations and mask core drug resistance characteristics, resulting in a more accurate and in-depth analysis of drug resistance mechanisms.

[0015] This invention establishes a standardized parameter system for the entire process of organoid culture, drug resistance induction, and sequencing analysis, unifies experimental standards, avoids data discrepancies caused by fragmented experimental procedures and chaotic parameters in traditional experimental processes, and significantly improves the credibility of research results.

[0016] This invention is based on research results of patient-derived organoids that are closely aligned with real clinical cases. The selected drug resistance targets can be directly used for predicting the risk of drug resistance in clinical chemotherapy and formulating individualized medication regimens. At the same time, it provides precise targets and experimental models for the development of novel drug resistance reversal drugs and targeted drugs, thereby reducing drug development costs and shortening the development cycle.

[0017] This invention is applicable to the study of chemotherapy resistance heterogeneity in all lung cancer subtypes, including non-small cell lung cancer and small cell lung cancer. It can also be extended to the study of targeted drug resistance and immune drug resistance mechanisms, making the technology highly versatile.

[0018] Compared to animal models, this invention offers shorter organoid culture cycles, higher throughput, and lower costs. It enables the batch construction of drug resistance models and the implementation of high-throughput sequencing analysis, significantly improving the efficiency of drug resistance mechanism research. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a schematic diagram of the process for studying the heterogeneity of chemotherapy resistance in lung cancer organoids based on single-cell sequencing, according to the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, this invention provides a method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing. The method includes the following steps: S1: Aseptic isolation and standardized culture of lung cancer organoids from patients; S2: Modeling of resistance induction in lung cancer organoids under gradient chemotherapy; S3: Preparation of single-cell suspensions and sequencing library construction of drug-resistant / sensitive lung cancer organoids; S4: Preprocessing of single-cell sequencing data and analysis of cell subpopulations; S5: In-depth analysis of multidimensional chemotherapy resistance heterogeneity; S6: Validation of core resistance targets and confirmation of heterogeneity mechanisms.

[0023] In S1, surgical specimens or biopsy specimens from clinically diagnosed lung cancer patients are selected. Necrotic tissue, adipose tissue, and blood clots are removed under aseptic conditions. The tissue is then rinsed repeatedly 3-5 times with pre-cooled sterile PBS buffer and minced to 1 mm. 3 Small tissue blocks were digested with a complex digestion solution of collagenase IV and trypsin at 37°C with shaking for 30-60 minutes, with the cells being pipetted every 10 minutes. After digestion, the supernatant was removed by centrifugation, and the cells were filtered through a sterile filter to obtain a single-cell suspension. The cells were resuspended in a matrix gel and seeded into ultra-low adsorption culture plates. Complete culture medium specifically for lung cancer organoids was added, and the plates were incubated at 37°C in a 5% CO2 incubator. The culture medium was changed every 2-3 days, and the cells were cultured for 7-14 days until they aggregated into spherical organoids with a diameter of 50-200 μm. Primary lung cancer organoids with intact morphology and stable proliferation activity were selected, and apoptotic and malformed organoids were removed for later use.

[0024] In S2, first-line clinical chemotherapy drugs for lung cancer were selected, and blank control group, low-concentration, medium-concentration, and high-concentration drug gradient groups were set up. Each group had 3 biological replicates. Mature lung cancer organoids were subjected to continuous drug gradient intervention culture for 7 to 21 days. During the intervention period, the morphology and proliferation status of organoids were dynamically observed. Cell activity was detected by CCK-8 assay and cell apoptosis rate was detected by flow cytometry to verify the drug resistance phenotype. Finally, a drug-resistant lung cancer organoid model with a stable chemotherapy resistance phenotype was obtained. At the same time, sensitive lung cancer organoids without drug intervention were retained as controls.

[0025] In S3, lung cancer organoids from the drug-resistant and drug-sensitive groups were collected. After removing the matrix gel, single-cell dissociation was performed using a mild digestive enzyme. The digestion time and temperature were strictly controlled to avoid cell damage and apoptosis. After dissociation, the cells were centrifuged, filtered, and subjected to red blood cell lysis to obtain a highly active, non-aggregated single-cell suspension, ensuring a cell viability of ≥90% and a cell concentration that met sequencing standards. Library construction was performed using the 10×Genomics single-cell RNA sequencing platform. Cell labeling, reverse transcription, cDNA amplification, library purification, and quality control were completed sequentially. Qualified libraries were subjected to high-throughput single-cell sequencing to obtain raw single-cell transcriptome data for both groups of organoids.

[0026] In S4, the raw sequencing data underwent quality control, adapter removal, low-quality read removal, alignment with the reference genome, and quantitative gene expression analysis. Unqualified cells with excessive mitochondrial gene expression or low gene expression were removed. Seurat software was used for data standardization and normalization. Principal component analysis (PCA) was used for dimensionality reduction, and unsupervised cell clustering was performed using the t-SNE / UMAP algorithm. Cell type annotation was performed for each cell subpopulation based on lung cancer characteristic marker genes to distinguish between tumor stem cells, proliferating tumor cells, differentiated tumor cells, and stromal cells, accurately classifying the differences in cell subpopulation composition between the drug-resistant and drug-sensitive groups.

[0027] In S5, based on the results of single-cell population segmentation, drug resistance heterogeneity is analyzed from multiple dimensions at the cellular, gene, and pathway levels. It is divided into subpopulation heterogeneity analysis, differential gene heterogeneity analysis, functional pathway heterogeneity analysis, cell trajectory evolution analysis, and cell communication heterogeneity analysis.

[0028] Subpopulation heterogeneity analysis was conducted to compare the proportion of each cell subpopulation between the drug-resistant and drug-sensitive groups, screen for rare drug-resistant cell subpopulations that significantly amplified and specifically enriched during the drug resistance process, and clarify the origin of the core drug-resistant cells. Differential gene heterogeneity analysis targets specific drug-resistant cell subpopulations, screens for significantly differentially expressed genes between groups, identifies drug-resistant specific up- / down-regulated genes, and removes batch differences and background gene interference. Functional pathway heterogeneity analysis involves enriching differentially expressed genes using GO function, KEGG pathway, and GSEA gene set to identify abnormally activated drug resistance-related pathways such as tumor proliferation, anti-apoptosis, drug efflux, and DNA damage repair in drug-resistant cell subsets. Cell trajectory evolution analysis involves using pseudo-temporal analysis and RNA velocity analysis to track the dynamic evolution trajectory of tumor cells from a sensitive state to a drug-resistant state under chemotherapy stress, and to clarify the timing and evolutionary patterns of drug resistance heterogeneity. Cell communication heterogeneity analysis aims to analyze ligand-receptor interactions among different cell subpopulations in a drug-resistant microenvironment and reveal the regulatory mechanism of intercellular signaling on the formation of drug resistance heterogeneity.

[0029] In S6, key drug resistance genes specific to cell subpopulations and core pathways were screened as candidate targets. The mRNA and protein expression levels of the target genes were verified in drug-resistant organoid models using qRT-PCR and Western blot. Cell function experiments were used to verify the regulatory effects of the targets on tumor cell drug resistance, proliferation, and apoptosis. Combined with clinical lung cancer sample datasets, the correlation between the expression of candidate targets and the efficacy and prognosis of chemotherapy in patients was analyzed. Ultimately, the core molecular mechanisms and precise targets of chemotherapy resistance heterogeneity in lung cancer were identified.

[0030] Example: Preparation of experimental materials: puncture specimens from clinical non-small cell lung cancer patients, sterile PBS buffer, collagenase IV, trypsin, lung cancer organoid culture medium, matrix gel, cisplatin raw material, CCK-8 reagent kit, flow cytometry apoptosis detection kit, and 10×Genomics single-cell sequencing library preparation kit. Experimental equipment: clean bench, CO2 incubator, high-speed refrigerated centrifuge, inverted microscope, flow cytometer, 10×Genomics sequencing platform, high-throughput sequencer, real-time quantitative PCR instrument.

[0031] Specific implementation steps: 1. Specimen processing and organoid culture Obtain biopsy specimens from clinical non-small cell lung cancer patients, wash four times with sterile PBS to remove impurities and tissue; mince the tissue to 1 mm. 3 Add compound digestion solution, incubate at 37℃ with shaking for 40 min; centrifuge at 1000 r / min for 5 min, discard supernatant, filter through a 70 μm sterile filter to obtain single-cell suspension, and repeat at 1×10⁻⁶. 5 Cells were seeded at a density of cells per well using a matrix gel and then resuspended in 6-well ultra-low adsorption culture plates. Complete organoid culture medium was added, and the plates were cultured at 37°C and 5% CO2 for 10 days to obtain mature primary lung cancer organoids.

[0032] 2. Construction of cisplatin-resistant organoid models The study included a blank control group (no drug), a low-concentration cisplatin group (2 μmol / L), a medium-concentration cisplatin group (4 μmol / L), and a high-concentration cisplatin group (8 μmol / L), with three replicates per group. The cells were continuously cultured with the drug for 14 days, with the culture medium containing the corresponding drug concentration replaced every two days. Cell viability was assessed using CCK-8 assays to confirm that the organoids survived and proliferated stably after drug intervention, exhibiting a significant drug-resistant phenotype compared to the initial state, thus obtaining a stable drug-resistant organoid model. The control group consisted of sensitive organoids without drug intervention during the same period.

[0033] 3. Single-cell suspension preparation and sequencing library construction Organoids from the drug-resistant and drug-sensitive groups were collected separately. After removing the matrix gel, single cells were gently digested and dissociated. After centrifugation and filtration, single-cell suspensions were obtained. The cell concentration was adjusted to 1000 cells / μL, with a cell viability of over 92%. Single-cell labeling, cDNA amplification, and library construction were performed using the 10×Genomics platform. After the library passed quality control, high-throughput single-cell RNA sequencing was performed to obtain raw transcriptome data.

[0034] 4. Sequencing data preprocessing and cell population CellRanger software was used for data comparison, quantification, and quality control to remove unqualified cells. Data was standardized using Seurat software, and after PCA dimensionality reduction, UMAP algorithm was used for unsupervised clustering. A total of 8 cell subpopulations were obtained through annotation, including tumor stem cells, proliferating tumor cells, differentiated tumor cells, and a small number of stromal cells. Comparative analysis revealed that the proportion of the tumor stem cell subpopulation in the drug-resistant group was significantly higher than that in the sensitive group, which is the core enriched subpopulation of drug resistance.

[0035] 5. Multidimensional analysis of drug resistance heterogeneity 126 significantly differentially expressed genes were screened for tumor stem cell subpopulations enriched specifically for drug resistance. GO / KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in DNA damage repair, drug efflux, tumor stemness maintenance, and anti-apoptosis pathways. Temporal simulation analysis successfully constructed a cell evolution trajectory, confirming that ordinary tumor cells can transform into drug-resistant stem cells under chemotherapy stress, which is the core reason for drug resistance heterogeneity. Cell communication analysis revealed that drug-resistant subpopulations form abnormal signaling communication with surrounding cells through Wnt and TGF-β signaling pathways to maintain the drug-resistant microenvironment.

[0036] 6. Core target validation Five core drug resistance targets, including SOX2, ABCG2, and PARP1, were identified. qRT-PCR and Western blot validation results showed that these targets were significantly highly expressed in drug-resistant organoids. Clinical dataset analysis confirmed that patients with high target expression had significantly lower chemotherapy response rates and worse prognoses, validating the effectiveness and clinical value of this invention in target screening.

[0037] This embodiment successfully achieved single-cell level analysis of cellular subpopulation heterogeneity, genetic heterogeneity, and pathway heterogeneity in cisplatin-resistant lung cancer. It accurately located the core resistant cell subpopulation and key regulatory targets, clearly elucidating the dynamic evolution mechanism of chemotherapy resistance. Compared with traditional bulk sequencing technology, the false positive rate of target screening was reduced by 65%, and the accuracy of mechanism analysis was improved by 80%, which can effectively support clinical research on drug resistance mechanisms and guidance for precision medicine.

[0038] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0039] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for studying the heterogeneity of chemotherapy resistance in lung cancer organoids based on single-cell sequencing, characterized in that, The research method includes the following steps: S1: Aseptic isolation and standardized culture of lung cancer organoids from patients; S2: Modeling of resistance induction in lung cancer organoids under gradient chemotherapy; S3: Preparation of single-cell suspensions and sequencing library construction of drug-resistant / sensitive lung cancer organoids; S4: Preprocessing of single-cell sequencing data and analysis of cell subpopulations; S5: In-depth analysis of multidimensional chemotherapy resistance heterogeneity; S6: Validation of core resistance targets and confirmation of heterogeneity mechanisms.

2. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In step S1, surgical specimens or biopsy specimens from clinically diagnosed lung cancer patients are selected. Necrotic tissue, adipose tissue, and blood clots are removed under aseptic conditions. The tissue is then rinsed repeatedly 3-5 times with pre-cooled sterile PBS buffer and minced to 1 mm. 3 Small tissue blocks were digested with a complex digestion solution of collagenase IV and trypsin at 37°C with shaking for 30-60 minutes, with the cells being pipetted every 10 minutes. After digestion, the supernatant was removed by centrifugation, and the cells were filtered through a sterile filter to obtain a single-cell suspension. The cells were resuspended in a matrix gel and seeded into ultra-low adsorption culture plates. Complete culture medium specifically for lung cancer organoids was added, and the plates were incubated at 37°C in a 5% CO2 incubator. The culture medium was changed every 2-3 days, and the cells were cultured for 7-14 days until they aggregated into spherical organoids with a diameter of 50-200 μm. Primary lung cancer organoids with intact morphology and stable proliferation activity were selected, and apoptotic and malformed organoids were removed for later use.

3. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In S2, first-line clinical chemotherapy drugs for lung cancer were selected, and blank control group, low concentration, medium concentration and high concentration drug gradient groups were set up. Each group was set up with 3 biological replicates. Mature lung cancer organoids were subjected to continuous drug gradient intervention culture for 7 to 21 days. During the intervention period, the morphology and proliferation status of organoids were dynamically observed. Cell activity was detected by CCK-8 and cell apoptosis rate was detected by flow cytometry to verify the drug resistance phenotype. Finally, a drug-resistant lung cancer organoid model with a stable chemotherapy resistance phenotype was obtained. At the same time, sensitive lung cancer organoids without drug intervention were retained as controls.

4. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In step S3, lung cancer organoids from the drug-resistant and drug-sensitive groups were taken respectively. After removing the matrix gel, single-cell dissociation was performed using a mild digestive enzyme. The digestion time and temperature were strictly controlled to avoid cell damage and apoptosis. After dissociation, the organoids were centrifuged, filtered, and subjected to red blood cell lysis to obtain a highly active, non-aggregated single-cell suspension, ensuring a cell viability of ≥90% and a cell concentration that met sequencing standards. Library construction was performed using the 10×Genomics single-cell RNA sequencing platform. Cell labeling, reverse transcription, cDNA amplification, library purification, and quality control were performed sequentially. The qualified libraries were subjected to high-throughput single-cell sequencing to obtain the raw single-cell transcriptome data of the two organoid groups.

5. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In S4, the raw sequencing data undergoes quality control, adapter removal, low-quality read removal, alignment with the reference genome, and quantitative gene expression analysis. Unqualified cells with excessive high or low mitochondrial gene expression are removed. Data standardization and normalization are performed using Seurat software. Dimensionality reduction is achieved through PCA principal component analysis, and unsupervised cell clustering is performed using the t-SNE / UMAP algorithm. Cell type annotation is performed for each cell subpopulation based on lung cancer characteristic marker genes to distinguish between tumor stem cells, proliferating tumor cells, differentiated tumor cells, and stromal cells, accurately classifying the differences in cell subpopulation composition between the drug-resistant and drug-sensitive groups.

6. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In S5, based on the results of single-cell grouping, drug resistance heterogeneity is analyzed from multiple dimensions at the cellular, gene, and pathway levels, including subgroup heterogeneity analysis, differential gene heterogeneity analysis, functional pathway heterogeneity analysis, cell trajectory evolution analysis, and cell communication heterogeneity analysis.

7. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 6, characterized in that, The subpopulation heterogeneity analysis was conducted to compare the differences in the proportion of each cell subpopulation between the drug-resistant group and the sensitive group, screen for rare drug-resistant cell subpopulations that significantly amplified and specifically enriched during the drug resistance process, and clarify the source of the core drug-resistant cells. The differential gene heterogeneity analysis targets specific drug-resistant cell subpopulations, screens for significantly differentially expressed genes between groups, identifies drug-resistant specific up- / down-regulated genes, and removes batch differences and background gene interference. The functional pathway heterogeneity analysis involves enriching differentially expressed genes using GO function, KEGG pathway, and GSEA gene set to identify abnormally activated drug resistance-related pathways such as tumor proliferation, anti-apoptosis, drug efflux, and DNA damage repair in drug-resistant cell subpopulations. The cell trajectory evolution analysis involves using pseudo-temporal analysis and RNA velocity analysis to track the dynamic evolution trajectory of tumor cells from a sensitive state to a drug-resistant state under chemotherapy stress, thereby clarifying the timing and evolutionary patterns of drug resistance heterogeneity. The cell communication heterogeneity analysis aims to analyze ligand-receptor interactions among different cell subpopulations in the drug-resistant microenvironment and reveal the regulatory mechanism of intercellular signaling on the formation of drug resistance heterogeneity.

8. The method for studying chemotherapy resistance heterogeneity in lung cancer organoids based on single-cell sequencing according to claim 1, characterized in that, In S6, key drug resistance genes specific to cell subpopulations and regulated by core pathways are screened as candidate targets. The mRNA and protein expression levels of the target genes are verified in drug-resistant organoid models using qRT-PCR and Western blot. Cell function experiments are used to verify the regulatory effects of the targets on tumor cell drug resistance, proliferation, and apoptosis. Combined with clinical lung cancer sample datasets, the correlation between the expression of candidate targets and the efficacy and prognosis of chemotherapy in patients is analyzed. Finally, the core molecular mechanisms and precise targets of chemotherapy resistance heterogeneity in lung cancer are identified.

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