A subscription to JoVE is required to view this content. Sign in or start your free trial.
* These authors contributed equally
This protocol outlines a comprehensive mass cytometry (cytometry by time-of-flight [CyTOF]) analysis method for evaluating both systemic and local immune responses in hepatocellular carcinoma (HCC). The approach aims to provide insights into the immune landscape of HCC, offering a deeper understanding of the tumor microenvironment and the associated immune mechanisms.
Hepatocellular carcinoma (HCC) is one of the most common and deadliest forms of liver cancer worldwide. Despite advances in treatment, the prognosis for HCC patients remains poor due to the complex interplay of genetic, environmental, and immunological factors driving its progression. Understanding the immune landscape of HCC is crucial for developing effective therapies, particularly in the field of immunotherapy, which holds great promises for improving patient outcomes. This study employs mass cytometry (cytometry by time-of-flight [CyTOF]) technology to investigate both systemic and local immune responses in patients with HCC. By analyzing peripheral blood and tumor samples, the research aims to identify unique immune cell populations, and their functional states associated with HCC progression. The findings provide a comprehensive overview of the immune landscape in HCC, highlighting potential biomarkers and therapeutic targets. This approach offers valuable insights into the immune mechanisms underlying HCC and paves the way for the development of more effective immunotherapies for this malignancy.
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and a significant global health issue due to its high incidence and mortality rates1. According to the World Health Organization, HCC ranks as the fifth most common cancer and the second leading cause of cancer-related deaths worldwide2. It is particularly prevalent in regions with high rates of chronic hepatitis B and C infections, such as East Asia and sub-Saharan Africa3. Major risk factors include viral hepatitis, cirrhosis, and metabolic syndrome4. HCC requires long-term treatment, imposing substantial physical and financial burdens, underscoring the need for effective prevention, early detection, and innovative treatment strategies5.
The immune system plays a crucial role in the development of HCC. The liver is an immunologically active organ with an abundance of immune cells, including liver-resident macrophages, natural killer (NK) cells, and T cells, which are essential for monitoring and eliminating abnormal cells6. However, HCC can evade immune surveillance by expressing immunosuppressive molecules, recruiting immunosuppressive cells, and altering the tumor microenvironment7,8. This immune escape not only promotes tumor growth and metastasis but also affects the response to immunotherapy9,10.
Systemic and local immune responses in the tumor microenvironment are key factors influencing cancer progression and therapeutic outcomes. Systemic immune responses involve circulating immune cells that can recognize and attack distant tumor cells, such as peripheral T cells, NK cells, and monocytes that can target tumor cells throughout the body. Local immune responses focus on immune cell activity within the tumor microenvironment, including tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and regulatory T cells (Tregs). While TILs often exert cytotoxic effects against tumor cells, TAMs and Tregs typically contribute to an immunosuppressive environment that supports tumor growth11,12. Tumor cells and stromal cells can reshape the tumor microenvironment to promote immunosuppression and evade immune surveillance. The interaction between systemic and local immune responses determines the overall effectiveness of anti-tumor immunity11. Understanding this interaction can aid in developing more effective immunotherapy strategies.
Traditional flow cytometry and immunohistochemistry, while widely used in immunological studies, exhibit significant limitations when it comes to analyzing complex immune landscapes due to their inability to perform comprehensive, high-dimensional analysis. Flow cytometry is highly effective for detecting surface and functional markers at the single-cell level; however, its capacity for simultaneous multi-marker analysis is restricted, often limited by spectral overlap and practical constraints on the number of fluorescent tags that can be used13,14. Immunohistochemistry, on the other hand, provides valuable insights into the tissue context of specific markers, but it is similarly hampered by the limited number of analyzable markers and the inherent difficulties of achieving robust, quantitative, high-dimensional assessments15.
To effectively characterize complex immune environments, high-dimensional techniques like mass cytometry (cytometry by time-of-flight [CyTOF]) are essential. Mass cytometry is an advanced technology that employs mass spectrometry to analyze multiple protein markers in single cells. It enables multiparametric analysis of individual cells without the spectral overlap issues seen in traditional flow cytometry16. By using metal-tagged antibodies, it can measure dozens of markers simultaneously, offering a comprehensive and unbiased view of cellular phenotypes and functions17. For example, Gadalla et al. developed a CyTOF panel with more than 40 parameters for the analysis of peripheral blood mononuclear cells (PBMC) and tumor tissue, demonstrating its advantage in high-dimensional immunophenotyping18. Traditional flow cytometry, with its limited number of detectable parameters, was unable to identify these rare cell populations exhibiting unique phenotypes. In contrast, mass cytometry enabled a comprehensive evaluation of the functional states of these cells, providing a more detailed and robust characterization. Behbehani et al. utilized mass cytometry to analyze bone marrow samples from patients with myelodysplastic syndromes (MDS), successfully identifying and characterizing rare aberrant hematopoietic progenitor cells18. The ability of mass cytometry to simultaneously detect over 40 surface and intracellular markers significantly enhanced the detection of these low-frequency cell subsets19. These capabilities overcome traditional limitations and provide deeper insights into immune landscapes, driving progress in immunology and therapeutic development. The ability to comprehensively profile cellular phenotypes and functions at the single-cell level greatly advances the understanding of immunological processes and aids in the development of targeted therapies.
Mass cytometry provides comprehensive insights into the systemic and local immune cell populations in HCC by simultaneously detecting multiple protein markers. This technology can distinguish between various types of T cells within the tumor microenvironment, such as effector T cells, regulatory T cells (Tregs), and exhausted T cells, elucidating their specific roles in tumor progression. By utilizing mass cytometry, researchers can identify immune markers associated with HCC prognosis20. For instance, T cell subsets with high Programmed Cell Death Protein 1 (PD-1) expression can serve as predictors of a patient's response to immune checkpoint inhibitors21. Additionally, it facilitates the discovery of new therapeutic targets by identifying specific immunosuppressive molecules, thereby providing a foundation for personalized treatment strategies. Technology's ability to detect multiple markers and its single-cell resolution make it particularly advantageous for uncovering novel therapeutic targets and designing combination immunotherapies. This advanced approach holds significant potential for improving treatment outcomes in HCC patients by offering a detailed understanding of the immune landscape and enabling the development of tailored therapeutic interventions.
This study aims to utilize mass cytometry to analyze the systemic and local immune cell profiles of patients with HCC. The objectives are to characterize the immune cell populations, correlate these characteristics with clinical outcomes and therapeutic responses, and identify specific immune markers and cell subsets associated with HCC prognosis. By elucidating the roles of various immune cells in treatment responses, this study seeks to provide a foundation for personalized treatment strategies. The findings are expected to optimize existing immunotherapies and offer valuable insights for developing new treatments, ultimately aiming to improve overall survival and quality of life for HCC patients.
The steps for blood and HCC sample collection, peripheral blood mononuclear cells (PBMCs) isolation, single-cell dissociation, and staining are outlined in the following plan. The experimental reagents and materials are all listed in the Table of Materials. All experiments were carried out with the approval of the Ethics Committee of the First Affiliated Hospital, School of Medicine, Zhejiang University, ensuring that the collection of tumor samples did not interfere with pathological diagnosis. Written informed consent was obtained from all human subjects.
1. Isolation of PBMCs
2. Isolation of tumor tissue cells
NOTE: The method for the isolation of tumor tissue cells was adapted from Song et al.22.
3. Cisplatin staining
4. Fc receptor blocking
5. Incubation of membrane protein antibodies
6. Nucleus protein staining
7. Cell fixation
8. Nuclear Intercalation Staining
9. Preparation of cell suspension
10. Mass cytometry and data analysis
To elucidate the immunological characteristics associated with HCC, a comprehensive analysis of immune cell populations was conducted. Paired PBMCs and HCC tissue samples were collected from 4 patients with HCC. Mass cytometry profiling was performed to examine immune cell populations at the single-cell proteomic level, using two antibody panels for both PBMCs and HCC tissue samples.
After quality control, 45,326 cells were incl...
This study leverages mass cytometry technology to provide an in-depth analysis of both systemic and local immune responses in HCC. The application of mass cytometry in this context enables the simultaneous detection of multiple markers at a single-cell level, offering a detailed immunophenotypic characterization that is crucial for understanding the complex immune landscape of HCC. Mass cytometry has revolutionized immunological studies by facilitating high-dimensional single-cell analysis. This technique employs rare me...
The authors declare that they have no conflicts of interest.
This work was supported by the National Key Research and Development Program of China (grant 2019YFA0803000 to J.S.), the Excellent Youth Foundation of Zhejiang Scientific (grant R22H1610037 to J.S.), the National Natural Science Foundation of China (grant 82173078 to J.S.), the Natural Science Foundation of Zhejiang Province (grant 2022C03037 to J.S.).
Name | Company | Catalog Number | Comments |
1×PBS | HyClone | SH30256.01 | |
10×PBS | HyClone | SH30256.01 | |
100 mm×20 mm tissue-culture-treated culture dish | Corning | 430167 | |
1000 mL pipette tips | Rainin | 30389218 | |
15 mL centrifuge tube | NEST | 601052 | |
200 mL pipette tips | Rainin | 30389241 | |
40 mm nylon cell strainer/70-mm nylon cell strainer | Falcon | 352340 | |
50 mL centrifuge tube | NEST | 602052 | |
70 μm syringe fifilter | Sangon Biotech | F613462-9001 | |
Anti-Human CCR2 Antibody (clone: K036C2) | BioLegend | 357224 | |
Anti-Human CCR3 Antibody (clone: 5E8) | BioLegend | 310724 | |
Anti-Human CCR7 Antibody (clone: G043H7) | BioLegend | 353240 | |
Anti-Human CD103 Antibody (clone: Ber-ACT8) | BioLegend | 350202 | |
Anti-Human CD115 Antibody (clone: 9-4D2-1E4) | BioLegend | 347314 | |
Anti-Human CD117 Antibody (clone: 104D2) | BioLegend | 313201 | |
Anti-human CD11b Antibody (clone: 1CRF44) | BD | 562721 | |
Anti-human CD11c Antibody (clone: B-ly6) | BD | 563026 | |
Anti-Human CD123 Antibody (clone: 6H6) | BioLegend | 306002 | |
Anti-Human CD127 Antibody (clone: A019D5) | BioLegend | 351337 | |
Anti-Human CD13 Antibody (clone: WM19) | BioLegend | 301701 | |
Anti-Human CD138 Antibody (clone: MI15) | BioLegend | 356535 | |
Anti-human CD14 Antibody (clone: HCD14) | BioLegend | 325604 | |
Anti-Human CD141 Antibody (clone: M80) | BioLegend | 344102 | |
Anti-Human CD15 Antibody (clone: QA19A61) | BioLegend | 376302 | |
Anti-human CD16 Antibody (clone: B7311) | BD | 561313 | |
Anti-Human CD161 Antibody (clone: HP-3G10) | BioLegend | 339902 | |
Anti-Human CD163 Antibody (clone: GHI/61) | BioLegend | 333603 | |
Anti-Human CD169 Antibody (clone: 7-239) | BioLegend | 346002 | |
Anti-human CD19 Antibody (clone: HIB19) | BioLegend | 302226 | |
Anti-Human CD1c Antibody (clone: L161) | BioLegend | 331501 | |
Anti-Human CD20 Antibody (clone: 2H7) | BioLegend | 302301 | |
Anti-Human CD206 Antibody (clone: 15-2) | BioLegend | 321151 | |
Anti-Human CD24 Antibody (clone: ML5) | BioLegend | 311129 | |
Anti-Human CD25 Antibody (clone: BC96) | BioLegend | 302624 | |
Anti-human CD3 Antibody (clone: UCHT1) | BD | 555916 | |
Anti-Human CD31 Antibody (clone: W18200D) | BioLegend | 375902 | |
Anti-Human CD32 Antibody (clone: FUN-2) | BioLegend | 303232 | |
Anti-Human CD326 Antibody (clone: CO17-1A) | BioLegend | 369812 | |
Anti-Human CD33 Antibody (clone: WM53) | BioLegend | 303402 | |
Anti-human CD4 Antibody (clone: L200) | BD | 563094 | |
Anti-Human CD45 Antibody (clone: HI30) | BD | 563716 | |
Anti-Human CD45RO Antibody (clone: UCHL1) | BioLegend | 304220 | |
Anti-human CD56 Antibody (clone: 5.1H11) | BioLegend | 362510 | |
Anti-Human CD64 Antibody (clone: S18012C) | BioLegend | 399502 | |
Anti-Human CD66b Antibody (clone: 6/40c) | BioLegend | 392917 | |
Anti-human CD68 Antibody (clone: Y1/82A) | BioLegend | 333808 | |
Anti-Human CD69 Antibody (clone: FN50) | BioLegend | 310902 | |
Anti-Human CD7 Antibody (clone: 4H9/CD7) | BioLegend | 395602 | |
Anti-human CD8 Antibody (clone: RPA-T8) | BD | 557750 | |
Anti-Human CD80 Antibody (clone: W17149D) | BioLegend | 375402 | |
Anti-Human CD86 Antibody (clone: BU63) | BioLegend | 374202 | |
Anti-Human FOXP3 Antibody (clone: 206D) | BioLegend | 320101 | |
Anti-Human HLA_ABC Antibody (clone: W6/32) | BioLegend | 311426 | |
Anti-human HLA-DR Antibody (clone: L243) | BioLegend | 307650 | |
Anti-Human IgD Antibody (clone: IA6-2) | BioLegend | 348211 | |
Anti-Human Ki67 Antibody (clone: Ki-67) | BioLegend | 350501 | |
Anti-Human PD_L2 Antibody (clone: MH22B2) | BD | 567783 | |
Anti-Human PD1 Antibody (clone: EH12.2H7) | BioLegend | 329951 | |
Anti-Human PDL1 Antibody (clone: MIH2) | BioLegend | 393602 | |
Anti-human TCR-γδ Antibody (clone: B1) | BD | 740415 | |
Cell cryopreservation solution | Thermo Fisher | A2644601 | |
Cell-lD Cisplatin | Standard BioTools | 201064 | |
Cell-lD Intercalator-lr | Standard BioTools | 201192A | |
Collagenase, Type IV | Gibco | 17104019 | |
Constant-temperature shake | FAITHFUL | FS-50B | |
CyTOF System | Fluidigm Corporation | Helios | |
Cytosplore | Cytosplore Consortium | 2.3.1 | |
Dispase II | Gibco | 17105041 | |
DNase I | Merck | DN25 | |
Eppendorf centrifuge | Eppendorf | 5702 | |
EQ Four Element Calibration Beads | Standard BioTools | 201078 | |
FBS | Gibco | 16000-044 | |
Ficoll-paque | Cytiva | 17-1440-02 | |
Finnpipette | Thermo Scientific | 4700870 | |
Fixation buffer | Thermo Scientific | FB001 | |
FlowJo | BD Life Sciences | 10.1 | |
Formaldehyde solution | Thermo Scientific | 28906 | |
Granzyme B Antibody, anti-human/mouse (clone: QA16A02) | BioLegend | 396413 | |
Heparin Tubes | BD | 367874 | |
Human BD Fc Block 2.5 mg/mL | BD | 564220 | |
MACS Tissue Storage Solution | Miltenyi | 130-100-008 | |
Maxpar Fix and Perm Buffer | Standard BioTools | 201067 | |
Maxpar metal-coniugated antibodies | Standard BioTools | Various | |
Maxpar PBS | Standard BioTools | 201058 | |
Maxpar Water | Standard BioTools | 201069 | |
Maxpare Cell Staining Buffer | Standard BioTools | 201068 | |
Metal-conjugated Anti-Human α-SMA Antibody (clone: 1A4) | Miltenyi Biotec | 130-098-145 | |
Percoll | Merck | P4937-500ML | |
Permeabilization buffer | Thermo Scientific | 00833356 | |
RBC lysis buffer | BD | 555899 | |
Refrigerated centrifuge | Eppendorf | 5910ri | |
RPMI 1640 medium | GE HealthCare | SH30027.0 | |
Scalpel | APPLYGEN | TB6298-1 | |
Sterile Pasteur pipette | ZDAN | ZD-H03 | |
Tissue digestion solution | Yeasen Biotech | 41423ES30 | |
Tuning Solution | Standard BioTools | 201072 | |
Vortex Mixer | Thermo Scientific | 88882012 |
Request permission to reuse the text or figures of this JoVE article
Request PermissionThis article has been published
Video Coming Soon
Copyright © 2025 MyJoVE Corporation. All rights reserved