
Advanced Instrumentation: The platform features 4 multicolor flow cytometers equipped with high-speed 96-well plate samplers to meet diverse experimental requirements, supporting the successful completion of over 3,000 projects.
Expertise in Panel Design: Our experienced technical team specializes in multicolor flow cytometry panel design and has established over 50 validated detection panels.
Automated Processing: Automated tissue processing equipment ensures highly stable and reliable experimental data.
Quality Assurance: Standard operating procedures (SOPs) guarantee strict data accuracy and traceability.
Absolute antigen quantification
Antibody binding assays
ADCC, ADCP, and CDC assays
Antibody endocytosis assays
Cell cycle analysis
Cell proliferation assays
Cell killing assays
Immune Cell Profiling: Multiple validated panels available, supporting up to 20 colors per panel.
Tissue Sample Analysis: Spleen, thymus, bone marrow, lymph nodes, skin, kidney, liver, lung, brain, small intestine, etc.
Tumor-Infiltrating Lymphocyte (TIL) Detection: High-dimensional profiling with up to 20 colors per panel.
Pharmacodynamic (PD) Biomarkers: Receptor occupancy (RO) assays and CAR-T cell tracking.
CBA multiplex cytokine assays
Intracellular cytokine staining (ICS)
Red blood cell (RBC) and erythropoiesis analysis
Platelet activation and aggregation analysis
Fluorescence-Activated Cell Sorting (FACS)
GemPharmatech offers a comprehensive suite of in vitro assays to accelerate early-stage drug discovery. Our capabilities include tumor target screening, precise antibody-binding affinity characterization, in vitro efficacy evaluation (including ADCC, ADCP, and CDC), as well as robust assays for cell proliferation, cell cycle, and apoptosis.
Mouse immune profiles are dynamically modulated by gene knockouts, humanized target modifications, and therapeutic treatments. We comprehensively assess the murine immune system by analyzing the frequency and absolute count of T cells, B cells, NK cells, monocytes, granulocytes, macrophages, dendritic cells (DCs), and other subpopulations in peripheral blood and lymphoid organs. Flow cytometric profiling of these diverse immune cell populations helps identify target cell types affected by genetic modifications or therapies, providing critical insights into drug mechanisms of action.
Tumor-infiltrating lymphocytes (TILs) are key immune components within the tumor microenvironment (TME), primarily comprising cytotoxic T cells, helper T cells, regulatory T cells (Tregs), NK cells, and myeloid populations.
The role of TILs in oncology is twofold:
Anti-tumor Effector Functions: Specific TIL subpopulations recognize and eliminate tumor cells. For example, cytotoxic T cells identify tumor-associated antigens and directly induce tumor lysis by releasing cytotoxic molecules such as perforin and granzyme B, while NK cells mediate direct tumor destruction.
Pro-tumor Immunosuppression: Conversely, certain populations within the TME, such as Tregs, suppress anti-tumor immunity and facilitate immune escape. Additionally, tumor-associated macrophages (TAMs) often polarize into an M2 phenotype, promoting tumor growth, angiogenesis, and metastasis.
Thymus: Characterization of T-cell differentiation and developmental stages.
Lymph Nodes: Profiling of T cells, B cells, and dendritic cells (DCs).
Bone Marrow: Evaluation of B-cell development, erythropoiesis, and hematopoietic stem cells (HSCs).
Kidney: Assessment of inflammatory cell infiltration in nephritis or injury models.
Lung: Analysis of immune cell infiltration in pulmonary tumor models or inflammatory disease states.
Skin: Characterization of inflammatory cellular infiltrates in dermatitis or wound models.
Brain: Profiling of neuroinflammatory and microglia populations within brain tissue.
Small Intestine: Isolation and analysis of intraepithelial lymphocytes (IELs) and lamina propria mononuclear cells (LPMCs).
Engraftment of human CD34+ hematopoietic stem cells (HSCs) in NCG-X mice successfully reconstitutes human erythroid lineages. This advanced model serves as a powerful platform for thalassemia and hematological disease research by enabling the precise quantification of human red blood cells and hemoglobin variants in the bone marrow and peripheral blood.
Cytometric Bead Array (CBA) is a flow-cytometry-based multiplex assay that enables the simultaneous quantification of multiple soluble proteins (such as cytokines and chemokines) within a single small-volume sample. The platform utilizes capture-antibody-coated beads with distinct fluorescence intensities. Once incubated with samples (e.g., serum, plasma, culture supernatant, or cell lysate) and PE-conjugated detection antibodies, the sandwich complexes are resolved on a flow cytometer. Protein concentrations are calculated by comparing PE fluorescence intensities against a software-generated standard curve. Compared to conventional ELISA, the CBA assay is highly sample-efficient and offers a streamlined, high-throughput workflow.
Cytek Full-Spectrum Flow Cytometers: Equipped with 3 lasers, supporting up to 38 detection channels.
Thermo Attune NxT Flow Cytometers: Equipped with 4 lasers, supporting up to 14 detection channels with acoustic focusing technology.

Fig1. A: Tumor cell antigen screening to identify tumor cells with high EGFR expression. B: Analyze the binding activity of Opdivo or Keytruda on Jurkat-hPD1 cells.
Fig2. A: Cellular internalization experimental procedure. B: Endocytosis of DS-8201a in SK-BR-3 cells was detected by flow cytometry.
Population | Gating Step | ||||
Total Leukocytes | mCD45+ | / | / | / | / |
Myeloid | mCD45+ | mCD11b+ | / | / | / |
Macrophages | mCD45+ | mCD11b+ | F4/80+/- | / | / |
M1 | mCD45+ | mCD11b+ | F4/80+/- | MHCII hi | CD206 lo |
M2 | mCD45+ | mCD11b+ | F4/80+/- | MHCII lo | CD206 hi |
Neutrophils | Not Macrophages | mLy6G+ | mLy6C lo | / | / |
Monocytes | Not Macrophages | mLy6G- | mLy6C hi | / | / |
DC | Not Macrophages | MHCII+ | mCD11c+ | / | / |
T cells | mCD45+ | mCD11b- | mCD3+ | / | / |
NK cells | mCD45+ | mCD11b- | mCD335+ | / | / |
B cells | mCD45+ | mCD11b- | mCD19+ | / | / |
Th | mCD45+ | mCD11b- | mCD3+ | mCD4+ | mCD8- |
Tc | mCD45+ | mCD11b- | mCD3+ | mCD4- | mCD8+ |
Treg | mCD45+ | mCD11b- | mCD3+ | mCD4+ | mCD25+FOXP3+ |

Fig3. Gating strategy for immune cell subpopulation clustering.
Population | Gating Step | |||
Human Leukocytes | hCD45+mCD45- | / | / | / |
Mouse Leukocytes | hCD45-mCD45+ | / | / | / |
T cells | hCD45+mCD45- | hCD3+ | / | / |
Th | hCD45+mCD45- | hCD3+ | hCD4+hCD8- | / |
Tc | hCD45+mCD45- | hCD3+ | hCD4-hCD8+ | / |
Treg | hCD45+mCD45- | hCD3+ | hCD4+hCD8- | hCD25+hCD127lo |
B cells | hCD45+mCD45- | hCD19+ | / | / |
Myeloid cells | hCD45+mCD45- | hCD33+ | / | / |
Monocytes | hCD45+mCD45- | hCD33+ | hCD14+ | / |
Granulocytes | hCD45+mCD45- | hCD33+ | hCD66b+ | / |
Neutrophils | hCD45+mCD45- | hCD33+ | hCD66b+ | hCD16+ |
DC | hCD45+mCD45- | hCD33+ | hCD14- | hHLA-DR+ |
Macrophages | hCD45+mCD45- | hCD33+ | hCD68+ | / |

Fig4. Gating strategy for immune cell subpopulation in the NCG-M-HSC model.
Population | Gating Step | ||||
Total Leukocytes | Live single | mCD45+ | / | / | / |
NK Cells | Live single | mCD45+ | mCD3- | mCD335+ | / |
T Cells | Live single | mCD45+ | mCD3+ | mCD335- | / |
Th | Live single | mCD45+ | mCD3+ | mCD4+ | mCD8- |
Tc | Live single | mCD45+ | mCD3+ | mCD4- | mCD8+ |
Treg | Live single | mCD45+ | mCD3+ | mCD4+ | mCD25+ mFOXP3+ |
Myeloid Cells | Live single | mCD45+ | mCD11b+ | / | / |

Fig5. Gating strategy for immune cell subpopulation in TILs.
Population | Gating Step | |||
Leukocytes | mCD45+ | / | / | / |
Macrophages | mCD45+ | MERTK+ | / | / |
Alveolar macrophages | mCD45+ | MERTK+ | CD11b-Siglec-F+ | / |
Interstitial macrophages | mCD45+ | MERTK+ | CD11b+Siglec-F- | / |
Neutrophils | Not Macrophage | mCD11b+ | Ly6G+ | / |
Monocytes | Not Neutrophils | mCD11b+ | Ly6C hi | / |
Eosinophils | Not monocytes | mCD11b+ | SSC-H hi | / |
DC | Not eosinophils | CD11b+CD11c+ | / | / |
T cells | Not eosinophils | mCD3+ | mCD19- | / |
Th | Not eosinophils | mCD3+ | mCD19- | CD4+CD8- |
Tc | Not eosinophils | mCD3+ | mCD19- | CD4-CD8+ |
B cells | Not eosinophils | mCD19+ | mCD3- | / |
Conventional B | Not eosinophils | mCD19+ | mCD3- | mCD23+ |
Analysis of mCD69 in T and B cells | ||||

Fig6. Gating strategy for immune cell subpopulation in lung tissue.
Population | Gating Step (14 colors) | ||||
Total Leukocytes | mCD45+ | / | / | / | / |
T cells | mCD45+ | mCD3+mCD19- | / | / | / |
B cells | mCD45+ | mCD3-mCD19+ | / | / | / |
Th | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | / | / |
Tc | mCD45+ | mCD3+mCD19- | mCD4-mCD8+ | / | / |
Memory T | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | mCD44+mCD62L+ | Analysis in Th and Tc |
Effector T | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | mCD44+mCD62L- | |
Naive T | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | mCD44-mCD62L+ | |
Treg | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | mCD25+FOXP3 | / |
Th1 | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | T-bet+ | / |
Th17 | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | R0RγT+ | / |
Tfh | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | mPD1+mCXCR5+ | / |
Th2 | mCD45+ | mCD3+mCD19- | mCD4+mCD8- | GATA3+ | / |

Fig7. Gating strategy for immune cell subpopulation in intestinal tissue, including Th cell subsets Th1, Th2, Th17, and Treg.
Population | Gating step | ||||
Total Leukocytes | Live single | mCD45+ | / | / | / |
Neutrophils | Live single | mCD45+ | mCD11b+ | Ly6G+ | / |
Monocytes | Live single | Not Neutrophiles | mCD11b+ | Ly6C hi | / |
Eosinophils | Live single | Not monocytes | mCD11b+ | SSC-H hi | / |
Macrophages | Live single | Not eosinophils | mCD11b+ | F4/80+ | / |
T cells | Live single | Not eosinophils | mCD3+ | mCD19- | / |
B cells | Live single | Not eosinophils | mCD19+ | mCD3- | / |
Th | Live single | Not eosinophils | mCD3+ | mCD4+ | mCD8- |
Tc | Live single | Not eosinophils | mCD3+ | mCD4- | mCD8+ |

Fig8. Gating strategy for immune cell subpopulation in brain.

Fig9. Gating strategy for red blood cells.

Fig10. The expression of TNF-α and IL-6 was significantly increased after drug treatment.

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