A mini review of recent development of flow cytometry in China

Flow cytometry has become a hot research area in China in recent years. Statistical results show that the papers published in Cytometry Part A with contributing authors from China has increased to 31% of all published research articles in 2021, as compared to 13% in 2020, approximately. This is paralleled by an increase in the acceptance rate that is now equivalent to Europe and United States (Figure 1). The increased research contributions from China are due to the trend that science and technology of cytometry is becoming a frontier multidisciplinary research area [1, 2], drawing a lot of research interest from both academia and industry. Here in this August issue, we have a collection of articles from our contributors in China that cover a plethora of interesting research topics including mass cytometry, label-free methods, and deep learning. It thus offers us an opportunity to briefly review the recent development of cytometry in China. In conventional flow cytometry, the cells are flowing in a fluidic channel and are hydrodynamically focused. While in the living body, blood vessels may serve as the flow channel for in vivo flow cytometry [3]. Wei (Who is also associate Editor of Cytometry Part A) et al. [4] reported in China their developments for counting circulation tumor cells (CTC) by in vivo flow cytometry approximately 10 years ago. Interestingly, they have applied the optical trapping technique for the manipulation of red blood cells in the capillaries of living mice [5]. As a further development of in vivo flow cytometry, Wei's group has studied the mesenchymal stem cells (MSC) in healthy and tumor carrying mice, which may in future be used for the development of MSCbased anti-cancer treatment strategies [6]. Recently, Wei et al. [7] have found that the release of CTCs may be regulated by the circadian rhythm in rodents with their in vivo flow cytometry technology. Light scattering spectra measurements have been demonstrated in traditional flow cytometry for label-free cell analysis [8]. The cells however are irregular, heterogenous and versatile, which are challenging to be characterized by the one-dimensional label-free light scattering spectroscopic measurements. The detection of light scattering patterns with imaging sensors may provide rich information for labelfree cell analysis. Su et al. [9] have developed the pattern recognition cytometry for label-free cell classifications by the integration of twodimensional (2D) light scattering patterns with machine learning technologies. This label-free light scattering cytometric technique has been further developed with deep learning strategy by our group (XS, AT) [10, 11]. Extracellular vesicles (EVs) are important for the understanding of tumor development and treatment, Su et al. [12] have demonstrated the label-free characterization of small EVs by light scatter imaging. Several other research groups in China are also developing novel cytometric technologies. Li et al. [13] have recently reported their development of three-dimensional (3D) hydrodynamic focusing system with on-chip optical design to enable white blood cell analysis with a microfluidic cytometer. Wang et al. [14] reported their development of 3D cell focusing system with standing acoustic waves on a sheathless microflow cytometer. The on-chip capture and isolation of cells, and long-term in situ imaging, were demonstrated by Huang's group [15]. Some other attractive quantitative cell analysis methods have been reported in nanotechnology. Chen et al. [16] showed the use of Forster Resonance Energy Transfer (FRET) imaging with optical section structure illumination microscopy for the analysis of living cells. With a home-built stochastic optical reconstruction microscopy (STORM), Qu et al. [17] explored the nanoliposome fine structures that may aid the development of liposome-based photodynamic therapy for cancer treatment. Jin et al. [18] have demonstrated their development of single nanoparticle imaging methods for the diagnosis of prostate cancer. Mass cytometry can discriminate around 100 labels by using metal isotopes as tags, measuring the atomic mass of the labels and eliminating the cross-talks from the signal channels, which is a limiting factor in fluorescence flow cytometry. High-dimensional mass cytometry data analysis methods and the imaging mass cytometric techniques that were developed in the past 10 years have made the mass cytometry popular in several research areas such as immunology and molecular biology [19, 20]. Here, Xianting Ding and colleagues report their work on imaging mass cytometry for study of the interesting visualization challenge of bacteria and cell phenotyping on the identical histological section (Feng et al., pp. 616–628). Their results of bacterial correlations to breast cancer cells may find future applications in tumor pathogenesis. Compared with the label-free light scattering cytometry, impedance cytometry that measures the bioelectrical parameters of cells has demonstrated wide applications for label-free cell classification [21]. As you can read in this issue, Jian Chen and colleagues study the inherent bioelectrical parameters of leukocytes (granulocytes, monocytes, and lymphocytes) and granulocytes (neutrophils, eosinophils, and basophils) in two articles (Tan et al. pp. 629–637; Wang et al., pp. 638–646), respectively, by using their recently developed constriction-microchannel based impedance flow cytometry. Here the inherent bioelectrical parameters are cell diameter, specific membrane capacitance and cytoplasmic conductivity measured from thousands of cells, rather than those impedance parameters that are dependent Accepted: 15 July 2022