The study of circulating cells in the blood stream is critical, as it covers many fields of biomedicine, including immunology, cell biology, oncology, and reproductive medicine. In-vivo flow cytometry (IVFC) is a new tool to monitor and count cells in real time for long durations in their native biological environment. This review describes two main categories of IVFC, i.e., labeled and label-free IVFC. It focuses on label-free IVFC and introduces its technological development and related biological applications. Because cell recognition is the basis of flow cytometry counting, this review also describes various methods for the classification of unlabeled cells, including the latest machine learning-based technologies.

The fluorescence-based in vivo flow cytometry (IVFC) is an emerging tool to monitor circulating cells in vivo. As a noninvasive and real-time diagnostic technology, the fluorescence-based IVFC allows long-term monitoring of circulating cells without changing their native biological environment. It has been applied for various biological applications (e.g., monitoring circulating tumor cells). In this work, we will review our recent works on fluorescence-based IVFC. The operation principle and typical biological applications will be introduced. In addition, the recent advances in IVFC flow cytometry based on photoacoustic effects and other label-free detection methods such as imaging-based methods, diffuse-light methods, hybrid multimodality methods and multispectral methods are also summarized.

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 coe±cient and the mean-squared error (MSE).