药物治疗后肝细胞癌类器官的生成及单细胞转录组分析
2026年5月26日
该方案概述了一种简化的肝细胞癌类器官生成方法，应用于药物治疗，并在治疗前后进行单细胞RNA测序，以表征治疗相关的转录变化。
我们重点关注HCC类器官对药物治疗的反应，以及它们在工作中的转化研究如何变化。该方案适用于HCC类器官，也可以适配到其他肿瘤类器官系统。首先，建立肝细胞癌或HCC患者来源的类器官，并为治疗扰动做准备。
按照制造商说明，将lenvatinib的液液用二甲基磺氧化物（DMSO）制成。使用前立即将存活液稀释至预定的工作浓度。准备足够的溶液，使所有井的最终体积相等，确保每口井的DMSO浓度一致。
接着，沿每个孔壁加入含lenvatinib的培养基，浓度为20微摩尔，以避免扰动基质穹顶。包含一个最终DMSO浓度相同的车辆控制。在标准培养条件下孵育类器官，按照预定的处理周期进行。
处理时间超过72小时时，应每48至72小时更换含药培养基，确保所有井的更换周期一致。治疗前记录基线明场图像。在处理过程中，使用相同的显微镜设置、放大倍率和视场位置，在固定间隔内获取图像。
针对形态学评估处理反应，应对所有图像使用相同的曝光设置、放大倍率和分析阈值。通过计算每个时间点每口井或田的总类器官面积，并将数值归一化为基线，来测量类器官面积增长曲线。要测量平均类器官直径，需确定单个完整类器官的直径，并计算每孔的平均值。
接下来，通过统计形态上可识别的完整类器官，排除任何碎片或坍缩碎片，确定存活的类器官数量。如需根据制造商说明进行额外的整体存活性评估，应在处理终点进行三维发光可行性分析。然后绘制类器官面积随时间的生长曲线。
比较载体处理组和伦伐替尼组的平均类器官直径。此外，比较不同处理组的存活类器官计数。确保使用一致的影像计划、纳入标准和分析参数，以确保可重复性。
选择具有完整三维形态、足够单细胞采集材料且无可见污染的类器官阱。在分离前，使用相同的显微镜设置、放大倍率和场选择标准，记录每个选定对象的明场图像。在对照组和处理组匹配时间点采集类器官，同时保持所有样本的板密度、处理计划和介质替换条件一致。
从每个孔中完全抽取培养基。用冰冷的PBS清洗每个井两次，以去除残留介质和杂质。每口井加入一毫升冰冷细胞回收液，并在冰上孵育20至30分钟。
培养期间每5至7分钟轻轻移液，以促进基质溶解。用宽孔径或切割的移液器尖端将溶解物质转移到预冷管中，以最大程度减少剪切应力。只有在基质大部分溶解且可见凝胶残留极少后，才进行酶解离。
用离心机在300加力下离心，在4摄氏度下悬浮五分钟。小心地去除上清液，不要扰动沉积物。将沉淀重新悬浮于一毫升重组细胞解离酶中，并在37摄氏度下培养5至10分钟。
使用宽口径的P1000吸头轻轻吸液8到10次，每两到三分钟，促进解离。当大多数类器官碎片分散成单细胞，只剩下小残余簇时，停止消化。接着，加入4毫升冰冷的PBS，含2%胎儿牛血清以停止消化。
然后，将悬浮液过滤在40微米的电池过滤器中。用PBS洗一次，去除残留的酶、聚集物和杂质。使用蓝锥排除法计数细胞数量。
确保细胞存活率达到85%或以上后，将最终细胞浓度调整为每微升700至1,200个细胞。排除有过多碎片、大量死细胞、大块可见聚集体或不完全解离的样本。如果有骨料，装载前重复过滤。
在相同条件下进行过程控制和处理样品，包括解离酶、消化时间、移液频率、过滤方法、细胞浓度和装载策略。最后，在单细胞文库扩增后，进行单细胞测序。类器官在标准三维培养条件下从恢复第一天存活并扩展。
第一天，类器官呈现为小型紧密结构，而第六天时，它们的体型增大且形态清晰。伦伐替尼处理的类器官体积较小，且形态变化显著，相较于DMSO处理的对照组。定量分析显示，与DMSO对照组相比，使用lenvatinib治疗后，平均类器官直径有所减少。
该方案使我们能够研究治疗后细胞状态、细胞组成和基因表达的变化。最大的挑战是保持细胞存活能力，保持所有组的简单处理一致性。在该程序下，可以进行细胞下游分析，包括聚类、差异基因实验分析、通路富集分析和资金推断。
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Overview
This article presents a standardized workflow for generating hepatocellular carcinoma (HCC) organoids, applying drug treatment, and performing single-cell RNA sequencing to analyze transcriptional changes before and after treatment. The protocol is robust, scalable, and adaptable to other tumor organoid systems, enabling detailed characterization of cellular composition and gene expression changes associated with drug exposure.
Key Study Components
Area of Science
- Cancer biology
- Organoid technology
- Single-cell transcriptomics
Background
- Hepatocellular carcinoma is a leading cause of cancer-related deaths worldwide.
- Marked intratumoral heterogeneity in HCC contributes to variable treatment responses and disease progression.
- Understanding transcriptional changes in tumor cells before and after drug treatment is critical for improving therapies.
- Existing workflows connecting organoid-based drug treatment with single-cell transcriptomic profiling are limited.
Purpose of Study
- To develop a reproducible protocol for drug treatment and single-cell RNA sequencing of HCC organoids.
- To enable side-by-side comparison of cellular and transcriptional changes in response to therapy.
- To provide technical guidance for improving reproducibility and sample quality in organoid studies.
Methods Used
- Establishment and expansion of HCC patient-derived organoids.
- Drug treatment with lenvatinib and vehicle control under standardized conditions.
- Imaging and quantitative assessment of organoid morphology, area, diameter, and viability.
- Single-cell dissociation, viability assessment, and preparation for single-cell RNA sequencing.
- Comparative analysis of single-cell transcriptomic data, including clustering and pathway enrichment.
Main Results
- Lenvatinib-treated organoids showed reduced size and altered morphology compared to controls.
- Quantitative analysis revealed a decrease in mean organoid diameter after drug treatment.
- The protocol preserved high cell viability and reproducibility across samples.
- Single-cell RNA sequencing enabled detailed analysis of cell state, composition, and gene expression changes post-treatment.
Conclusions
- The workflow provides a practical and scalable approach for studying drug responses in HCC organoids at single-cell resolution.
- It facilitates robust comparative analysis of transcriptional changes associated with therapy.
- The protocol is adaptable to other organoid systems and supports downstream cellular analyses.
Frequently Asked Questions
Executive Industry Relevance
Single-cell transcriptomic profiling of hepatocellular carcinoma (HCC) organoids before and after drug treatment addresses the challenge of intratumoral heterogeneity and variable therapeutic response. This workflow enables high-resolution analysis of cellular and transcriptional changes, supporting predictive confidence in early-stage oncology discovery. The protocol's scalability and adaptability position it as a reusable platform for portfolio-wide mechanistic de-risking and target validation.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of therapeutic hypotheses by capturing drug-induced transcriptional shifts at single-cell resolution.
- Supports biological de-risking through detailed mapping of cell state and composition changes post-treatment.
- Facilitates functional target validation by linking drug exposure to specific gene expression alterations.
- Provides predictive confidence for triaging candidate compounds based on mechanistic response profiles.
Screening & Assay Development
- Prepares validated organoid systems for downstream screening and quantitative assessment of drug effects.
- Standardizes imaging, viability, and morphological metrics to ensure reproducibility and assay reliability.
- Enables scalable, side-by-side evaluation of compound efficacy using robust, quantitative outputs.
- Supports platform reuse across different tumor organoid models for broader screening applications.
Translational & Preclinical Research
- Aligns in vitro drug response data with disease-relevant cellular heterogeneity observed in patient tumors.
- Provides continuity from discovery through preclinical validation by enabling comparative transcriptomic analysis.
- Supports risk-adjusted advancement decisions by revealing pathway-level and cell-type-specific drug effects.
- Enhances predictive de-risking for translational biomarker identification and validation.
Pipeline & Workflow Integration
This protocol integrates from early discovery through lead identification and preclinical research, bridging drug perturbation studies with high-content single-cell analytics.
- Discovery Biology: Supports hypothesis testing and pathway clarification by mapping drug-induced gene expression changes.
- Screening: Delivers reproducible, quantitative readouts for compound evaluation in organoid models.
- Analytics: Enables clustering, differential expression, and pathway enrichment analyses to compare treatment conditions.
- Translational Research: Connects in vitro findings to disease-relevant cellular diversity and biomarker discovery.
- Enterprise Reuse: Provides a standardized, adaptable workflow for repeated use across oncology portfolios.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in drug response studies.
- Operational Value: Delivers standardized, reproducible, and scalable workflows for organoid-based analytics.
- Strategic Value: Improves go/no-go decision-making and capital efficiency by enabling robust early-stage de-risking.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of oncology assets based on high-content data.
Implementation Considerations
- Requires expertise in organoid culture, single-cell dissociation, and transcriptomic library preparation.
- Demands access to advanced imaging, cell viability assessment, and single-cell sequencing infrastructure.
- Necessitates strict cross-team standardization of imaging, dissociation, and analysis parameters for reproducibility.
- Adaptable across different tumor organoid systems with attention to model-specific dissociation and viability challenges.
- Sample quality and cell viability are critical limitations that must be managed throughout processing.
