Reinders P P, van Vianen P H, van der Keur M, van Engen A, Janse C J, Tanke H J
Department of Cytochemistry and Cytometry, Medical Faculty, University of Leiden, The Netherlands.
Cytometry. 1995 Mar 1;19(3):273-81. doi: 10.1002/cyto.990190312.
A computer program is described for the automated analysis of data obtained by flow cytometry for in vitro antimalarial drug susceptibility testing. Samples of malaria-infected red blood cells (RBC), which were cultured in the presence of different concentrations of antimalarial drugs, were stained with Hoechst. The Hoechst fluorescence intensity of infected RBC corresponds to DNA content of the parasites and to their stage of development. After measurement of the samples by a FACStar flow cytometer equipped with a UV laser and an autosampler, FCS 1.0 data files were generated. The HP PAS-CAL program developed for these files identifies five different populations--uninfected RBC, infected RBC, free parasites, leukocytes, and debris--on the basis of their light scatter and fluorescence characteristics. The program calculates the percentage of infected cells, the total number of parasite nuclei, and the average number of nuclei per parasite. The results of each culture are presented as a drug dose-response curve. During data analysis, user interaction is limited to selecting the first file of the first culture. The algorithm then processes each culture automatically. Potential problems or difficulties in analysis are flagged. To date, a total of 862 drug tests have been evaluated and fall into two classes, an extended microtest and the World Health Organization standardized microtest. These tests gave satisfactory results in more than 99% of the cases.
描述了一种计算机程序，用于对通过流式细胞术获得的数据进行自动分析，以进行体外抗疟药物敏感性测试。在不同浓度抗疟药物存在下培养的疟疾感染红细胞（RBC）样本用Hoechst染色。感染的RBC的Hoechst荧光强度对应于寄生虫的DNA含量及其发育阶段。在用配备紫外激光和自动进样器的FACStar流式细胞仪对样本进行测量后，生成了FCS 1.0数据文件。针对这些文件开发的HP PAS - CAL程序根据其光散射和荧光特性识别五个不同的群体——未感染的RBC、感染的RBC、游离寄生虫、白细胞和碎片。该程序计算感染细胞的百分比、寄生虫核的总数以及每个寄生虫的平均核数。每种培养物的结果以药物剂量反应曲线表示。在数据分析过程中，用户交互仅限于选择第一种培养物的第一个文件。然后算法自动处理每种培养物。标记出分析中潜在的问题或困难。迄今为止，总共评估了862次药物测试，分为两类，扩展微量测试和世界卫生组织标准化微量测试。这些测试在超过99%的情况下给出了令人满意的结果。
