Establishment of genetically engineered gastric organoids with Cdh1-inactivation. (A) Genetic alteration of the KRAS, and TP53 genes based on the cBioPortal. n represents the total number of patients enrolled in each subtype. DGAC, diffuse-type gastric adenocarcinoma; SRCC, signet ring cell carcinoma; TAC, tubular adenocarcinoma; STAD, stomach adenocarcinoma; MAC, mucinous adenocarcinoma; PAC, papillary adenocarcinoma. (B) Illustration of the workflow for stomach tissue collection and dissociation, gene manipulation of the gastric organoids (GOs), GOs culture, and representative image of GOs. Three GO lines were generated, including WT, KP, and EKP. WT mice and KP mice were sacrificed to collect stomach tissue. After removing the forestomach, stomach tissue was dissociated into a single cell and culture as organoids. Adeno-Cre virus was used to treat KrasLSL-G12D; Trp53fl/fl organoids to generate KP organoids, followed by nutlin-3 selection. After selection, EKP organoids were generated using CRISPR-mediated Cdh1 KO from KP GOs. (C) Representative images of WT, KP, and EKP GOs at passage day 8. Scale bars: 200 μm. (D) Growth analysis for WT, KP, and EKP GOs in two passages on day 8 of each passage. P values were calculated using the one-way ANOVA; error bars: SD. Numbers below each label represent the number of organoids. (E) H&E staining of WT, KP, and EKP GOs. Scale bars: 50 μm (left panels); 200 μm (right panels). (F) MKI67 staining of WT, KP, and EKP GOs (n = 5). Scale bars: 50 μm. (G) CDH1 staining of WT, KP, and EKP GOs (n = 5). Scale bars: 50 μm. (H) Statistics analysis of MKI67 staining (Fig. 3 F). P values were calculated using the one-way ANOVA; error bars: SD. The representative images are shown. (I) Batch-based UMAPs of WT, KP, and EKP GOs. The Harmony integration package was used to remove the batch effect. (J) Leiden-based clustering UMAPs of WT, KP, and EKP GOs. Cell clusters were named by the most highly variable genes. (K) Cell proportion analysis of WT, KP, and EKP GOs. Each color represents a different cell type. The color code is based on the cell types shown in Fig. 3 J. (L) Batch-based and Scissor-based UMAP of WT and EKP GOs generated by the Scissor package. TCGA datasets of normal stomach and DGAC patients were utilized. (M) Cluster-based and Scissor-based UMAP of EKP GOs generated by Scissor package. DGAC1 and DGAC2 datasets were utilized to perform the comparison. (N) Dot plots of EKP GOs of DGAC1 and DGAC2 molecular signatures. The top 50 highly variable genes were used to calculate the molecular signature of each DGAC subtype. Gene list is shown in Table S8. P values were calculated by Mann–Whitney testing. ns: P > 0.05; *: P ≤ 0.05; **: P ≤ 0.01; ***: P ≤ 0.001. All data are derived from two or more independent experiments with the indicated number of mice.
Figure 3.

Establishment of genetically engineered gastric organoids with Cdh1-inactivation. (A) Genetic alteration of the KRAS, and TP53 genes based on the cBioPortal. n represents the total number of patients enrolled in each subtype. DGAC, diffuse-type gastric adenocarcinoma; SRCC, signet ring cell carcinoma; TAC, tubular adenocarcinoma; STAD, stomach adenocarcinoma; MAC, mucinous adenocarcinoma; PAC, papillary adenocarcinoma. (B) Illustration of the workflow for stomach tissue collection and dissociation, gene manipulation of the gastric organoids (GOs), GOs culture, and representative image of GOs. Three GO lines were generated, including WT, KP, and EKP. WT mice and KP mice were sacrificed to collect stomach tissue. After removing the forestomach, stomach tissue was dissociated into a single cell and culture as organoids. Adeno-Cre virus was used to treat KrasLSL-G12D; Trp53fl/fl organoids to generate KP organoids, followed by nutlin-3 selection. After selection, EKP organoids were generated using CRISPR-mediated Cdh1 KO from KP GOs. (C) Representative images of WT, KP, and EKP GOs at passage day 8. Scale bars: 200 μm. (D) Growth analysis for WT, KP, and EKP GOs in two passages on day 8 of each passage. P values were calculated using the one-way ANOVA; error bars: SD. Numbers below each label represent the number of organoids. (E) H&E staining of WT, KP, and EKP GOs. Scale bars: 50 μm (left panels); 200 μm (right panels). (F) MKI67 staining of WT, KP, and EKP GOs (n = 5). Scale bars: 50 μm. (G) CDH1 staining of WT, KP, and EKP GOs (n = 5). Scale bars: 50 μm. (H) Statistics analysis of MKI67 staining (Fig. 3 F). P values were calculated using the one-way ANOVA; error bars: SD. The representative images are shown. (I) Batch-based UMAPs of WT, KP, and EKP GOs. The Harmony integration package was used to remove the batch effect. (J) Leiden-based clustering UMAPs of WT, KP, and EKP GOs. Cell clusters were named by the most highly variable genes. (K) Cell proportion analysis of WT, KP, and EKP GOs. Each color represents a different cell type. The color code is based on the cell types shown in Fig. 3 J. (L) Batch-based and Scissor-based UMAP of WT and EKP GOs generated by the Scissor package. TCGA datasets of normal stomach and DGAC patients were utilized. (M) Cluster-based and Scissor-based UMAP of EKP GOs generated by Scissor package. DGAC1 and DGAC2 datasets were utilized to perform the comparison. (N) Dot plots of EKP GOs of DGAC1 and DGAC2 molecular signatures. The top 50 highly variable genes were used to calculate the molecular signature of each DGAC subtype. Gene list is shown in Table S8. P values were calculated by Mann–Whitney testing. ns: P > 0.05; *: P ≤ 0.05; **: P ≤ 0.01; ***: P ≤ 0.001. All data are derived from two or more independent experiments with the indicated number of mice.

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