High-dimensional cytometry technologies allow for comprehensive analysis at single cell level for biological discovery, but the complex nature of the resulting datasets makes it challenging to fi nd relevant fi ndings. In a substudy of Children's Oncology Group (COG) AALL1621, we applied B-cell lineage tumor-centric CyTOF panel to relapsed/refractory B-cell precursor acute lymphoblastic leukemia patients (n = 28) who were treated with inotuzumab ozogamicin (InO), antiCD22 monoclonal antibody bound to anti-tumor calicheamicin, in order to explore therapeutic predictors. The cellcommunity clustering provided candidates of leukemia cell population that would distinguish poor responders from favorable responders to InO. Through tumor kinetic analysis of single cell dataset combining clinical information and cluster identifi ers of unique leukemia profi les, we fi nally achieved CD22^lowBcl-2^high as a dual-marker signature that reliably predicts poor response to InO. Furthermore, this approach also demonstrates dynamic changes of tumor composition after InO and suggests Bcl-2 inhibitor as a promising drug with the potential for synergistic clinical effi ciency. Here, we provide a detailed description of single cell phenotyping pipeline composed of dimensional reduction, cell-community clustering with force-directed layout, and generation of single cell matrix dataset, which is applicable to general multiparametric cytometry data, leading to comprehensive understanding of single cell data.

Monoclonal antibodies are an essential biological material for the development of test drugs in medical biology and pharmaceuticals, but their production is fraught with numerous difficulties. In particular, the limited availability of commercial monoclonal antibodies suitable for flow cytometry analysis has slowed the progress of research.

The MIHS method developed by our group is a state-of-the-art monoclonal antibody production technique that combines hybridoma technology with flow cytometry. This method is an innovative technique for targeting antigens with physiological structures and is particularly useful for fl ow cytometric analysis. In this review, the MIHS method is presented, which can be easily implemented by laboratories skilled in the operation of fl ow cytometers.

Extracellular vesicle (EV) is a membrane-bound particle released from almost all cell types. EVs are thought to play an important role in cell-to-cell communication because they functionally transport biologically active proteins, lipids, and nucleic acids. Because EVs are abundantly expressed in body fl uids, analysis of EVs in body fl uids can be a novel biomarker for disease. The most notable method for measuring EVs has been the technique of fl ow cytometry (FCM). A standardization study was initiated in 2010 by the Standardization Committee of the International Society of Thrombosis and Haemostasis. The primary concerns for EV measurement by FCM are the accuracy and reproducibility of the measurement and how to minimize differences in data between different FCM models and between institutions. In 2020, the study by Welsh was an important milestone in the standardization of FCM EV measurements, as it confi rmed that seemingly disparate results are equivalent after calibration. The multicenter standardization effort, which took more than

10 years, has reached a certain point of completion.

Research using single cell RNA-Seq (scRNA-Seq) analysis has become an essential method in the life sciences. Single cell analysis technology refers to the ability to study individual cells at the molecular level, allowing researchers to gain insights into the heterogeneity and dynamics of cellular populations. Although the technique was introduced more than a decade ago, it has been increasingly used in the last few years. The amount of RNA per cell is reported to range of 1-10 pg, with the specific value dependent on the cell type. The majority of this RNA consists of ribosomal RNA, while only approximately 1/100th of the total RNA is comprised of mRNA. Many researchers have developed technologies to obtain accurate information from such extremely small amounts of RNA. Currently, based on these technologies, experiments are often conducted using single cell analysis platforms that prioritize experimental stability, high reproducibility, and simplicity of experimental procedures. These platforms have the capability to analyze over 10,000 cells in a single experiment. Especially in the analysis of immune cell populations, it is often necessary to analyze minor populations, so the ability to analyze a large number of cells simultaneously and ensure consistent quality is highly desirable. Recently, in addition, spatial transcriptome analysis techniques, which also consider the spatial location of cells, have been extensively used in combination with single-cell analysis to elucidate the interactions between cells identified or characterized through single-cell analysis. This paper aims to present an overview of the prevailing single-cell analysis techniques.