Name: Generation and Single-Cell Transcriptomic Analysis of Hepatocellular Carcinoma Organoids following Drug Treatment (Video) | JoVE Journal
Uploaded: 2026-05-26T13:25:00.000+00:00
Duration: 8 min 18 s
Description: Learn to generate hepatocellular carcinoma organoids and analyze single-cell transcriptomic changes following drug treatment using patient-derived models. This scalable workflow enables robust characterization of therapy responses.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

This research was supported by the National Natural Science Foundation of China (82303022; 82122048; 82372714; 82203380), the Guangdong Basic and Applied Basic Research Foundation (2023A1515011416), and the Guangzhou Basic and Applied Basic Research Foundation (2024A04J6575). Some schematic elements were created using Home for Researchers (https://www.home-for-researchers.com) and were adapted by the authors. Schematic illustrations were created using Figdraw (https://www.figdraw.com) and were adapted by the authors.

<ol>
	<li>Villanueva, A. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hepatocellular+carcinoma.">Hepatocellular carcinoma.</a> N Engl J Med. 380 (15), 1450-1462 (2019).</li><li>Bray, F., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+cancer+statistics+2018:+GLOBOCAN+estimates+of+incidence+and+mortality+worldwide+for+36+cancers+in+185+countries.">Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.</a> CA Cancer J Clin. 68 (6), 394-424 (2018).</li><li>Sung, H., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+cancer+statistics+2020:+GLOBOCAN+estimates+of+incidence+and+mortality+worldwide+for+36+cancers+in+185+countries.">Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.</a> CA Cancer J Clin. 71 (3), 209-249 (2021).</li><li>Safri, F., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneity+of+hepatocellular+carcinoma:+from+mechanisms+to+clinical+implications.">Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications.</a> Cancer Gene Ther. 31 (8), 1105-1112 (2024).</li><li>Zhang, X. F., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+cell-state+plasticity+driven+by+FOXM1/CEBPB+axis+disrupts+developmental+heterogeneity+and+therapeutic+resistance+in+hepatocellular+carcinoma.">Targeting cell-state plasticity driven by FOXM1/CEBPB axis disrupts developmental heterogeneity and therapeutic resistance in hepatocellular carcinoma.</a> J Hepatol. 84 (2), 370-384 (2026).</li><li>Thorel, L., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patient-derived+tumor+organoids:+a+new+avenue+for+preclinical+research+and+precision+medicine+in+oncology.">Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology.</a> Exp Mol Med. 56 (7), 1531-1551 (2024).</li><li>Liu, Y., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+decade+of+liver+organoids:+advances+in+disease+modeling.">A decade of liver organoids: advances in disease modeling.</a> Clin Mol Hepatol. 29 (3), 643-669 (2023).</li><li>Li, L., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+primary+liver+cancer+organoids+reveal+intratumor+and+interpatient+drug+response+heterogeneity.">Human primary liver cancer organoids reveal intratumor and interpatient drug response heterogeneity.</a> JCI Insight. 4 (2), e121490 (2019).</li><li>Yang, H., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pharmacogenomic+profiling+of+intra-tumor+heterogeneity+using+a+large+organoid+biobank+of+liver+cancer.">Pharmacogenomic profiling of intra-tumor heterogeneity using a large organoid biobank of liver cancer.</a> Cancer Cell. 42 (4), 535-551.e8 (2024).</li><li>Xing, L., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-based+therapies+in+hepatocellular+carcinoma:+state+of+the+art+and+clinical+perspectives.">RNA-based therapies in hepatocellular carcinoma: state of the art and clinical perspectives.</a> Hepatoma Res. 10, 24 (2024).</li><li>Aissa, A. F., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+transcriptional+changes+associated+with+drug+tolerance+and+response+to+combination+therapies+in+cancer.">Single-cell transcriptional changes associated with drug tolerance and response to combination therapies in cancer.</a> Nat Commun. 12 (1), 1628 (2021).</li><li>Zhang, C. Y., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+and+optimization+of+a+human+hepatocellular+carcinoma+patient-derived+organoid+model+for+potential+target+identification+and+drug+discovery.">Development and optimization of a human hepatocellular carcinoma patient-derived organoid model for potential target identification and drug discovery.</a> J Vis Exp. (198), e65785 (2023).</li><li>Wang, Q., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Breakthroughs+and+challenges+of+organoid+models+for+assessing+cancer+immunotherapy:+a+cutting-edge+tool+for+advancing+personalised+treatments.">Breakthroughs and challenges of organoid models for assessing cancer immunotherapy: a cutting-edge tool for advancing personalised treatments.</a> Cell Death Discov. 11 (1), 222 (2025).</li><li>Yang, C., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+methods+of+different+tumor+organoids+and+their+application+in+tumor+drug+resistance+research.">Modeling methods of different tumor organoids and their application in tumor drug resistance research.</a> Cancer Drug Resist. 8, 32 (2025).</li><li>Denisenko, E., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+assessment+of+tissue+dissociation+and+storage+biases+in+single-cell+and+single-nucleus+RNA-seq+workflows.">Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.</a> Genome Biol. 21 (1), 130 (2020).</li><li>Yip, S., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Give+them+vasculature+and+immune+cells:+how+to+fill+the+gap+of+organoids.">Give them vasculature and immune cells: how to fill the gap of organoids.</a> Cells Tissues Organs. 212 (5), 369-382 (2023).</li><li>Zhou, C., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardization+of+organoid+culture+in+cancer+research.">Standardization of organoid culture in cancer research.</a> Cancer Med. 12 (13), 14375-14386 (2023).</li></ol>

The authors have no conflicts of interest to disclose.

To ensure reproducibility, efficiency, and high-quality single-cell transcriptomic analysis, several technical steps in this workflow require careful control. Consistency during revival and expansion of patient-derived HCC organoids is essential, because variations in seeding density, basement membrane matrix composition, and culture timing can influence organoid morphology, growth, and downstream treatment responses13,14. In addition, high cell viability during ...

Hepatocellular carcinoma (HCC) is the most common primary liver cancer in adults and a major cause of cancer-related deaths worldwide1,2,3. Its poor clinical outcome is due to significant intratumoral heterogeneity, leading to treatment resistance and recurrence4. A recent study indicates that developmental diversity and cell-state plasticity driven by key transcriptional programs such as the FOXM1/CEBPB axis contribute to therapeutic resistance in HCC5. These findings highlight the need to understand how tumor cell populations adapt and transition between states under sustained treatment pressure. However, most preclinical models do not adequately capture the dynamic, continuous nature of these treatment-associated state transitions, limiting mechanistic investigation of tumor adaptation and therapeutic failure.
Patient-derived organoids (PDOs) provide a useful platform for this purpose because they preserve important genomic alterations and cellular heterogeneity of the original tumor6. HCC organoids can be maintained in long-term culture while retaining key tissue features7, making them particularly suitable for treatment-response studies. In addition, organoid-based studies have demonstrated the feasibility of using liver cancer organoids for drug testing and for capturing interpatient and intratumoral differences in therapeutic sensitivity, supporting their value as preclinical models for treatment-response research8,9. However, most organoid&#8211;based drug studies still rely primarily on endpoint viability measurements or focus mainly on bulk phenotypic responses, thereby providing only limited information about heterogeneous cellular responses and treatment-associated transcriptional changes. In parallel, the rapid development of RNA-based therapeutic strategies for HCC further underscores the need for preclinical systems that can accurately predict and mechanistically dissect treatment responses at single-cell resolution10.
Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular composition and transcriptional states within organoid models11. When applied to organoids collected before and after treatment, scRNA-seq can reveal gene-expression changes, shifts in cell populations, and heterogeneous responses that are not captured by bulk assays. This approach therefore represents a practical and powerful strategy for investigating treatment-associated transcriptional remodeling in HCC organoids. However, a practical, reproducible workflow integrating HCC organoid culture, drug treatment, and pre- and post-treatment single-cell RNA sequencing remains lacking. Such a workflow is most effective when organoid integrity is maintained during drug exposure and sufficient viable cells are recovered for downstream single-cell profiling. As with other organoid-based approaches, important limitations include incomplete representation of stromal, vascular, and immune components of the tumor microenvironment, as well as potential selection bias during prolonged in vitro&#160;culture.
An integrated experimental workflow is described for generating HCC organoids, applying defined drug treatment, and processing organoid samples for single-cell RNA sequencing before and after treatment. This workflow encompasses organoid revival and expansion, pre-treatment quality assessment, controlled drug exposure, and downstream single-cell RNA sequencing of matched samples. The approach provides a practical framework for comparing, at single-cell resolution, treatment-associated changes in cellular composition and gene-expression programs in HCC organoids.

<table><tbody><tr><td>[Leu15]-gastrin I human</td><td>Merck</td><td>G9145</td><td></td></tr><tr><td>1.5 mL Microtubes</td><td>Merck</td><td>AXYMCT150LC</td><td></td></tr><tr><td>1X RBC Lysis Buffer</td><td>Thermo Fisher Scientific</td><td>00-4333-57</td><td></td></tr><tr><td>2.0ml Internal Thread Cryogenic Vials</td><td>Nest</td><td>607001</td><td></td></tr><tr><td>A8301 (TGFb inhibitor)</td><td>Tocris Bioscience</td><td>2939</td><td></td></tr><tr><td>AFP Polyclonal antibody</td><td>Proteintech</td><td>14550-1-AP</td><td></td></tr><tr><td>Anti-Albumin antibody [ALB/2144]</td><td>abcam</td><td>ab236492</td><td></td></tr><tr><td>Anti-HNF-4-alpha antibody [EPR16885] - ChIP Grade</td><td>abcam</td><td>ab181604</td><td></td></tr><tr><td>Anti-Prealbumin antibody [EPR3219]</td><td>abcam</td><td>ab92469</td><td></td></tr><tr><td>Anti-SOX9 antibody [EPR14335-78]</td><td>abcam</td><td>ab185966</td><td></td></tr><tr><td>B27 Supplement (503), minus vitamin A</td><td>Thermo Fisher Scientific</td><td>12587010</td><td></td></tr><tr><td>Cell Recovery Solution, 100 mL</td><td>Corning</td><td>354253</td><td>Reagent used to dissolve basement membrane matrix and recover organoids before replating or downstream processing</td></tr><tr><td>CellTiter-Glo&amp;reg;&amp;nbsp;3D Cell Viability Assay</td><td>Promega</td><td>G9681</td><td></td></tr><tr><td>CHIR99021</td><td>Merck</td><td>SML1046</td><td></td></tr><tr><td>Chromium Next GEM Single Cell 3&#039; GEM, Library &amp;amp; Gel Bead Kit v3.1</td><td>10x Genomics</td><td>CG000227</td><td></td></tr><tr><td>Corning Cell Strainer, 40 &amp;mu;m, Blue, S, IND, 1/50</td><td>Corning</td><td>431750</td><td></td></tr><tr><td>Costar 24-well Clear Flat Bottom Ultra-Low Attachment Multiple Well Plates, Individually Wrapped, Sterile</td><td>Corning</td><td>3473</td><td></td></tr><tr><td>Cultrex Reduced Growth Factor BME, Type 2 PathClear (BME)</td><td>Merck</td><td>3533-005-02</td><td>Extracellular matrix used to support three-dimensional organoid culture and dome formation</td></tr><tr><td>DMSO</td><td>Merck</td><td>C6164</td><td>Solvent used to prepare drug stock solutions and vehicle control</td></tr><tr><td>Dulbecco&#039;s Modified Eagle Medium/Ham&#039;s F-12</td><td>Thermo Fisher Scientific</td><td>12634028</td><td>Advanced DMEM/F-12</td></tr><tr><td>E7080 (Lenvatinib)</td><td>Selleck</td><td>S1164</td><td>Tyrosine kinase inhibitor used for organoid treatment and therapeutic perturbation</td></tr><tr><td>Fetal Bovine Serum Value FBS</td><td>Gibco</td><td>A5256701</td><td></td></tr><tr><td>Forskolin</td><td>Tocris Bioscience</td><td>1099</td><td></td></tr><tr><td>GlutaMAX supplement</td><td>Thermo Fisher Scientific</td><td>35050061</td><td></td></tr><tr><td>Hematoxylin and Eosin Staining Kit</td><td>Beyotime</td><td>C0105S</td><td></td></tr><tr><td>HEPES, 1 M</td><td>Thermo Fisher Scientific</td><td>15630080</td><td></td></tr><tr><td>Leica DM6 B Fluorescence Motorized Microscope</td><td>Leica</td><td>N/A</td><td></td></tr><tr><td>N2 supplement (1003)</td><td>Thermo Fisher Scientific</td><td>17502048</td><td></td></tr><tr><td>N-acetylcysteine</td><td>Merck</td><td>A0737-5MG</td><td></td></tr><tr><td>Nicotinamide</td><td>Merck</td><td>N0636</td><td></td></tr><tr><td>Nunc 15 mL Conical Sterile Polypropylene Centrifuge Tubes</td><td>Thermo Fisher Scientific</td><td>339651</td><td></td></tr><tr><td>Penicillin/streptomycin (10,000 U/mL)</td><td>Thermo Fisher Scientific</td><td>15140122</td><td></td></tr><tr><td>Phosphate-Buffered Saline</td><td>GENOM BIO</td><td>GNM20012</td><td>Buffer used for washing organoids and preparing samples for downstream processing</td></tr><tr><td>Recombinant human EGF</td><td>Peprotech</td><td>AF-100-15</td><td></td></tr><tr><td>Recombinant human FGF10</td><td>Peprotech</td><td>100-26</td><td></td></tr><tr><td>Recombinant human HGF</td><td>Peprotech</td><td>100-39</td><td></td></tr><tr><td>Recombinant human Noggin</td><td>Peprotech</td><td>120-10C</td><td></td></tr><tr><td>Rho kinase inhibitor Y-27632 dihydrochloride</td><td>Merck</td><td>Y0503</td><td>ROCK inhibitor used to enhance organoid survival after thawing or passaging</td></tr><tr><td>R-spodin1-conditioned medium</td><td>(Broutier et al.)</td><td>N/A</td><td>Secretion of cell lines</td></tr><tr><td>Trypan Blue Stain, 0.4% membrane filtered Prepared in 0.85% saline</td><td>Gibco</td><td>15250061</td><td></td></tr><tr><td>TrypLE Express Enzyme (1x), no phenol red</td><td>Thermo Fisher Scientific</td><td>12604013</td><td>Trypsin substitute</td></tr><tr><td>Wnt-3a-conditioned medium</td><td>(Broutier et al.)</td><td>N/A</td><td>Secretion of cell lines</td></tr></tbody></table>

generation and single-cell transcriptomic analysis of hepatocellular carcinoma organoids following drug treatment

Generation and Single-Cell Transcriptomic Analysis of Hepatocellular Carcinoma Organoids following Drug Treatment

Hepatocellular carcinoma is a major cause of cancer-related mortality worldwide and is characterized by marked intratumoral heterogeneity, which contributes to variable treatment responses, tumor recurrence, and disease progression. A deeper understanding of how tumor cells change at the transcriptional level before and after drug treatment is therefore essential for improving therapeutic strategies. However, practical experimental workflows that connect organoid-based drug treatment with downstream single-cell transcriptomic profiling remain limited. In this study, a standardized workflow for generating hepatocellular carcinoma organoids, applying defined drug treatment, and performing single-cell RNA sequencing on samples collected before and after treatment is developed. The protocol includes organoid revival and expansion, pre-treatment quality assessment, drug exposure, organoid preparation for single-cell dissociation, library construction, and basic comparative analysis of single-cell transcriptomic data. Critical technical precautions are provided to improve reproducibility and sample quality. This workflow enables side-by-side characterization of cellular composition and transcriptional changes associated with drug treatment in hepatocellular carcinoma organoids. The protocol is robust, scalable, and adaptable across different organoid systems, providing a practical platform for investigating treatment-associated gene expression changes at single-cell resolution.

Figure 1 outlines the generation and characterization of hepatocellular carcinoma (HCC) organoids. In Figure 1A, patient-derived tumor tissues were used to establish organoid cultures for histological analysis and treatment studies. Figure 1B shows bright-field images of organoids at days 1 and 6 post-recovery, demonstrating survival and expansion under standard three-dimensional culture conditions. At day 1, organoids appear as sma...

This protocol outlines a streamlined method for generating hepatocellular carcinoma organoids, applying drug treatment, and performing single-cell RNA sequencing before and after treatment to characterize treatment-associated transcriptional changes.

Hepatocellular carcinoma is a major cause of cancer-related mortality worldwide and is characterized by marked intratumoral heterogeneity, which ...

We focus on how HCC organoids respond to drug treatment and how their translational studies change at work. This protocol is useful for HCC organoids, and can also be adapted to other tumor organoid systems. To begin, establish hepatocellular carcinoma or HCC-patient-derived organoids, and prepare them for therapeutic perturbation.Prepare the lenvatinib stock solution in dimethyl sulphoxide, or DMSO, according to the manufacturer's instructions. Dilute the stock solution in prewarm culture medium immediately before use to the predefined working concentration. Prepare enough solution to achieve equal final volumes in all wells, ensuring the same DMSO concentration across each well.Next, add lenvatinib-containing medium at 20 micromolar along the wall of each well to avoid disturbing the matrix domes. Include a vehicle control with the same final DMSO concentration. Incubate the organoids under standard culture conditions for the predefined treatment period.For treatments exceeding 72 hours, replace the drug-containing medium every 48 to 72 hours, ensuring a consistent replacement schedule across all wells. Record baseline bright-field images before treatment. Acquire images at fixed intervals during treatment using identical microscope settings, magnification and field positions.For morphology-based evaluation of the treatment response, use identical exposure settings, magnification and analysis thresholds for all images. Measure the organoid area growth curve by calculating the total organoid area, per well or field, at each time point, and normalizing the values to baseline. To measure the mean organoid diameter, determine the diameter of individual intact organoids and calculate the mean value per well.Next, determine the surviving organoid count by counting morphologically identifiable intact organoids, while excluding any debris or collapsed fragments. Perform a three-dimensional luminescence viability assay at the treatment endpoint if additional bulk viability assessment is required following the manufacturer's instructions. Then, plot the organoid area growth curves over time.Compare the mean organoid diameter between vehicle-treated and lenvatinib-treated groups. Also, compare surviving organoid counts across treatment groups. Ensure using consistent imaging schedules, inclusion criteria and analysis parameters to ensure reproducibility.Select organoid wells with intact 3D morphology, sufficient material for single cell capture, and no visible contamination. Record bright-field images of each selected well before dissociation using identical microscope settings, magnification and field selection criteria. Collect organoids at matched time points across control and treated groups, while maintaining identical plating density, treatment schedule, and medium replacement conditions across all samples.Aspirate the culture medium completely from each well. Wash each well twice with ice-cold PBS to remove residual medium and debris. Add one milliliter of ice-cold cell recovery solution to each well, and incubate on ice for 20 to 30 minutes.Pipette gently every five to seven minutes during incubation to facilitate matrix dissolution. Transfer the dissolved material into pre-chilled tubes using a wide bore or cut pipette tip to minimize sheer stress. Proceed to enzymatic dissociation only after the matrix is largely dissolved and minimal visible gel residue remains.Centrifuge the recovered organoid suspension at 300 G for five minutes at four degrees Celsius. Remove the supernatant carefully without disturbing the pellet. Re-suspend the pellet in one milliliter of recombinant cell dissociation enzyme, and incubate at 37 degrees Celsius for five to 10 minutes.Pipette gently eight to 10 times every two to three minutes, using a wide bore P1000 tip, to promote dissociation. Stop the digestion when most organoid fragments have dispersed into single cells, and only small residual clusters remain. Next, add four milliliters of ice-cold PBS containing 2%fetal bovine serum to stop the digestion.Then, filter the suspension through a 40-micrometer cell strainer. Wash once with PBS to remove residual enzyme, aggregates and debris. Count the cells using trypan blue exclusion.After ensuring the cell viability of 85%or above, adjust the final cell concentration to 700 to 1, 200 cells per microliter. Exclude samples with excessive debris, abundant dead cells, large visible aggregates or incomplete dissociation. Repeat filtration before loading if aggregates are present.Process control and treated samples under identical conditions, including the dissociation enzyme, digestion time, pipetting frequency, filtration method, cell concentration, and loading strategy. Finally, after single-cell library amplification, perform single-cell sequencing. Organoid survived and expanded under standard three-dimensional culture conditions from day one to day six post-recovery.At day one, organoids appeared as small compact structures, whereas by day six, they exhibited increased size and well-defined morphology. Lenvatinib-treated organoids were smaller, and displayed altered morphology compared to DMSO-treated controls. Quantitative analysis showed a reduction in mean organoid diameter, following lenvatinib treatment, compared to DMSO controls.This protocol allows us to study changes in cell state, cellular composition and gene expression after treatment. The biggest challenge is preserving cell viability, keeping simple processing consistent across all groups. Cellular downstream analysis can be conducted following this procedure, including clustering, differential gene experiments analysis, pathway enrichment analysis, and treasury inference.

generation-single-cell-transcriptomic-analysis-hepatocellular

Research

JoVE Journal

Cancer Research

This protocol describes a semi-automated method for medium- to high-throughput organoid drug screenings and microscope-agnostic, automated image analysis software to quantify and visualize multiparametric, single-organoid drug responses to capture intratumor heterogeneity.

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

We provide a comprehensive overview and refinement of existing protocols for hepatocellular carcinoma (HCC) organoid formation, encompassing all stages of organoid cultivation. This system serves as a valuable model for the identification of potential therapeutic targets and the assessment of drug candidate effectiveness.

Development and Optimization of A Human Hepatocellular Carcinoma Patient-Derived Organoid Model for Potential Target Identification and Drug Discovery

Here, we introduce a comprehensive protocol for the generation and downstream analysis of human brain organoids using single-cell and single-nucleus RNA sequencing.

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

Organoids generated from patient tumors are orthotopically injected into the mouse liver. The resection of non-tumorous liver tissue leads to a regenerative environment in the liver tissue where the tumor is located.

A Hepatocellular Cancer Patient&#45;Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth

The recellularized extracellular matrix of a decellularized rat liver can be used as a humanized, three-dimensional ex vivo model to study the distribution and transgene expression of a virus or viral vector.

Study of Viral Vectors in a Three-dimensional Liver Model Repopulated with the Human Hepatocellular Carcinoma Cell Line HepG2

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Here we present a reproducible method for developing an orthotopic murine model of head and neck squamous cell carcinoma. Tumors demonstrate clinically relevant histopathological features of the disease, including necrosis, poor differentiation, nodal metastases, and immune infiltration. Tumor-bearing mice develop clinically relevant symptoms including dysphagia, jaw displacement, and weight loss.

Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment

Transarterial chemoembolization (TACE) is the standard therapy for patients in the intermediate stage of hepatocellular carcinoma and is typically performed through femoral artery access. Compared with transfemoral access, transradial access (TRA) can decrease the rate of bleeding complications and improve patient tolerance. A method is presented here to perform transarterial chemoembolization via radial artery access.

Transradial Access Chemoembolization for Hepatocellular Carcinoma Patients

Intrinsically disordered domains are important for oncogenic fusion transcription factor function. To therapeutically target these proteins, a more detailed understanding of the regulatory mechanisms employed by these domains is required. Here, we use transcriptomics to map important structural features of the intrinsically disordered EWS domain in Ewing sarcoma.

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Here, we present how to generate a non-alcoholic fatty liver disease (NAFLD)-associated Hepatocellular Carcinoma (HCC) zebrafish model to study the impact of cholesterol surplus on liver microenvironment and immune cell landscape.

Analysis of Liver Microenvironment During Early Progression of Non-Alcoholic Fatty Liver Disease-Associated Hepatocellular Carcinoma in Zebrafish

Learn to generate hepatocellular carcinoma organoids and analyze single-cell transcriptomic changes following drug treatment using patient-derived models. This scalable workflow enables robust characterization of therapy responses.

Generation and Single-Cell Transcriptomic Analysis of Hepatocellular Carcinoma Organoids following Drug Treatment

Explore More Videos