機器の初期設定は，機種により違いがあるため，慎重に設定を行う必要があると思われた。従来のクロスマッチとFlowDSA-XMで一致率は，Class1で96%（57/59症例），Class2で93%（55/59症例）であった。Class1，2の両者合わせて10%（6/59症例）でデータの不一致が確認された。LABScreen Single Antigen確認すると，判定保留域の結果を示した症例やHLA抗体以外の反応により従来法との間で結果に解離が生じたと推測された。
Peripheral blood contains non-cellular vesicles surrounded by phospholipid bilayers. These are collectively referred to as extracellular vesicles (EVs). Disease-associated EVs, such as tumor-derived EVs, are attracting attention as new biomarkers. We aimed to construct a method of quantifying EVs, and here we introduce the strategy developed in this research. The method consists of the simple steps of (1) purifi cation of the target EVs and (2) phospholipid bilayer staining. As a disease model, EVs were prepared from cultured cell supernatant, and an immunomagnetic positive separation technique was used for EV purifi cation. Specifi cally, superparamagnetic submicron particles were conjugated with an antibody against CD326 (EpCAM), which is expressed at high levels in many epithelial cancers, and an antibody against CD142 (tissue factor), which can cause thromboembolism. By selecting an appropriate lipid stain, it was possible to detect the target EVs with high sensitivity, and their positivity was easily judged: the strength of the signal from the stained lipid was correlated with the surface area of the EVs (independently of the amount of surface antigen).
Therefore, any ambiguity due to variation in EV size and the amount of antigen per particle was resolved. Here, we give examples of quantitative measurements using a conventional fl ow cytometry analyzer. We discuss the applicability of this strategy of EV purifi cation and phospholipid staining to an antibody array and imaging analysis that allows the simultaneous detection of multiple EVs.
Progress has been made in the treatment of multiple myeloma (MM), and a series of novel therapeutic agents, including antibody-based drugs such as elotuzumab and daratumumab, are available in the clinic. While fl ow cytometry (FCM) is a major method for MM diagnosis and evaluation of therapeutic effects, detecting MM cells after antibodybased therapies is challenging, as antibodies used for FCM sometimes recognize the same epitopes that are targeted by the therapeutic ones. As a result, FCM could fail to detect true MM clones. In this study, we examined the effi cacy and accuracy of the FCM-based diagnostic methods using an antibody targeting multiple epitopes of CD38 (CD38ME) and intracellular p63 as well as those targeting CD138 and CD38high. When we defi ned MM cells using antibodies against CD38ME and intracellular p63, proportions of MM cells were highly correlated with those defined by the conventional FCM methods using anti-CD38high and / or CD138 antibodies (r2 = 0.9967-0.9991). Interestingly, expression levels of CD38high and CD138 were signifi cantly low in MM cells obtained from antibody-treated individuals. In contrast, MM clones were accurately detected using antibodies against CD38ME and intracellular p63. Our data suggest that extra caution should be taken when MM cells obtained from patients treated with antibody-based therapies were evaluated by FCM. We propose that antibodies targeting CD38ME and/or intracellular p63 should be included in the antibody mixture for FCM-based detection of MM cells.
Chromosome doubling cells (CDCs) contain four sets of chromosomes. Tetraploid cells (CDCs originating from diploid cells) are recognized as precursor of cancers with chromosomal instability. CDCs arise in aneuploidy cells and facilitates epithelial-to-mesenchymal transition and more aggressive phenotypes. Cytometry is a useful technique in identifying the small number of CDCs arising in normal and cancer cells. However, cytometry technique measuring only DNA contents cannot distinguish CDCs. We developed the methods using image cytometry combined with Fluorescence in situ hybridization (FISH) for centromeres to identify the CDCs and elucidate proliferative capability by detecting CDC colonies. This article describes our developed methods and our fi ndings and discusses the importance of CDC analysis.