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Objective: Visualizing cell interactions in blood circulation is of great importance in studies of anticancer immunotherapy or drugs. However, the lack of a suitable imaging system hampers progress in this field., Methods: In this work, we built a dual-channel in vivo imaging flow cytometer to visualize the interactions of circulating tumor cells (CTCs) and dendritic cells (DCs) simultaneously in the bloodstream. Two artificial neural networks were trained to identify blood vessels and cells in the acquired images., Results and Conclusion: Using this technique, single CTCs and CTC clusters were readily distinguished by their morphology. Interactions of CTCs and DCs were identified, while their moving velocities were analyzed. The CTC-DC clusters moved at a slower velocity than that of single CTCs or DCs. This may provide new insights into tumor metastasis and blood rheology., Significance: This in vivo imaging flow cytometry system holds great potential for assessing the efficiency of targeting CTCs with anticancer immune cells or drugs.

The fate of circulating tumor cells is an important determinant of their ability to form distant metastasis. Here, we demonstrate the use of in vivo flow cytometry as a powerful new method for detecting quantitatively circulating cancer cells. We specifically examine the circulation kinetics of two prostate cancer cell lines with different metastatic potential in mice and rats. We find that the cell line and the host environment affect the circulation kinetics of prostate cancer cells, with the intrinsic cell line properties determining the initial rate of cell depletion from the circulation and the host affecting cell circulation at later time points.

Contact with antigen-presenting cells (APCs) initiates an activation cascade within T lymphocytes, including a rise in cytosolic calcium, lymphokine production, and cell division. Although T cell-APC physical contact is required for an immune response, little is known about the patterns of cellular interactions and their relation to activation. Calcium imaging combined with an optical trap enabled the T cell contact requirements and polarity to be investigated at the single-cell level. APCs or anti-CD3 mAb-coated beads were trapped with a laser and placed at different locations along the T cell, which has a polarized appearance defined by the shape and direction of crawling. T cells were 3-fold more sensitive to APC contact made at the leading edge of the T cell than with contact made at the tail. Anti-CD3 mAb-coated 6-micrometer beads induced calcium signaling with approximately 10-fold higher frequency and approximately 4-fold shorter latency on contact with the leading edge of the T cell than on contact with the trailing edge. Alterations in antibody density (2 to 500 per micrometer(2)) and bead size (1 to 6 micrometer in diameter) were used to determine the spatial requirements and the minimal number of receptors which must be engaged to transmit a positive signal. T cell response percentage, latency, and calcium-signaling pattern (transient vs. sustained or oscillatory) depended on antibody density on the bead. The presence of approximately 170 anti-CD3 mAb within the contact area elicited a detectable T cell calcium response. We propose here that engagement of no more than 340 T cell receptors (approximately 1% of the total on the cell) is sufficient to initiate Ca(2+) signaling. The minimal contact area was approximately 3 micrometer(2).

In biomedical research fields, the in vivo flow cytometry (IVFC) is a widely used technology which is able to monitor target cells dynamically in living animals. Although the setup of IVFC system has been well established, baseline drift is still a challenge in the process of quantifying circulating cells. Previous methods, i.e., the dynamic peak picking method, counted cells by setting a static threshold without considering the baseline drift, leading to an inaccurate cell quantification. Here, we developed a method of cell counting for IVFC data with baseline drift by interpolation fitting, automatic segmentation and wavelet-based denoising. We demonstrated its performance for IVFC signals with three types of representative baseline drift. Compared with non-baseline-correction methods, this method showed a higher sensitivity and specificity, as well as a better result in the Pearson's correlation coefficient and the mean-squared error (MSE). [ABSTRACT FROM AUTHOR]