研究目的
Visualizing interactions of circulating tumor cell and dendritic cell in the blood circulation to study anticancer immunotherapy and tumor metastasis.
研究成果
The dual-channel in vivo imaging flow cytometer successfully visualized interactions between CTCs and DCs, showing that CTC-DC clusters move slower than single cells, which may facilitate arrest on blood vessels and provide insights into tumor metastasis and immunotherapy efficiency. The system has potential for real-time image processing and assessment of anti-tumor therapies.
研究不足
The system is limited to veins due to lower frame rate (30 fps), as arterial flow velocities may be too high for detection. Field of view is 749.88 μm × 562.41 μm, and maximum measurable velocity is approximately 8.44 mm/s. Variations in optical properties of tissues may require adaptive training of neural networks.
1：Experimental Design and Method Selection:
Built a dual-channel in vivo imaging flow cytometer based on a modified fluorescence microscope with multiband filter sets and a color CCD camera. Used artificial neural networks for image processing to identify blood vessels and cells.
2：Sample Selection and Data Sources:
Male BALB/c mice (6-week-old, 20±2g) were used. Dendritic cells were isolated from mouse bone marrow, and 4T1 breast cancer cells were fluorescently labeled. Cells were injected intravenously into mice.
3：List of Experimental Equipment and Materials:
Leica DM5500 microscope with 10X water immersion objective (NA=
4：6), multiband filter set (LED-DA/FI/TR/Cy5-A-LDMK-ZERO, Semrock), color CCD camera (Micropublisher RTV 3, Qimaging), halogen lamp, MATLAB software (Version 2015a, Mathworks), RPMI-1640 media, fetal bovine serum, DiD dye, GFP-labeled cells, pentobarbital salt, depilatory cream, glycerin, heating pad. Experimental Procedures and Operational Workflow:
Anesthetized mice, removed hair from ears, adhered ear to slide with glycerin, placed on heated stage. Injected 4T1 cells and dendritic cells intravenously. Captured dual-channel images at 30 fps using the imaging system. Processed images with ANNs to identify blood vessels and cells, and computer vision algorithms to extract trajectories and velocities.
5：Data Analysis Methods:
Used MATLAB for statistical analysis, including student's t-test to compare velocities. ANNs were trained and tested for accuracy in identifying blood vessels and cells.
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Microscope
DM5500
Leica
Used as the base system for in vivo imaging, modified with additional components for dual-channel fluorescence imaging.
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Filter Set
LED-DA/FI/TR/Cy5-A-LDMK-ZERO
Semrock
Multiband filter set for illumination with blue and red light, and filtering emitted fluorescence.
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Serum
Fetal Bovine Serum
Thermo Fisher
Component of complete culture medium for cell culture.
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CCD Camera
Micropublisher RTV 3.3
Qimaging
Color CCD camera for capturing dual-channel images simultaneously at video rate (30 fps).
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Software
MATLAB Version 2015a
Mathworks
Used for constructing, training, and testing artificial neural networks, and for statistical analysis.
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Media
RPMI-1640
HyClone, GE Healthcare Life Sciences
Culture medium for cell isolation and maintenance.
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Dye
DiD
Invitrogen
Fluorescent label for dendritic cells, with excitation at 644 nm and emission at 665 nm.
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Protein
Green Fluorescence Protein
Fluorescent label for 4T1 breast cancer cells, with absorption at 488 nm and emission at 510 nm.
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