Automated analysis of GPI‐deficient leukocyte flow cytometric data using GemStone™
Automated analysis of GPI‐deficient leukocyte flow cytometric data using GemStone™
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使用 GemStone™ 自动分析 GPI 缺陷的白细胞流式细胞术数据
DOI：
10.1002/cyto.b.21024
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
C. B. Bagwell
中科院分区：
文献类型：
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作者：
David T Miller;B. Hunsberger;C. B. Bagwell
流式细胞术是检测阵发性睡眠性血红蛋白尿症（PNH）和相关疾病中糖基磷脂酰肌醇（GPI）缺陷克隆的标准方法。尽管国际临床细胞计数学会（ICCS）和国际PNH兴趣小组（IPIG）已经发布了PNH检测的指南，但数据分析尚未标准化。目前的分析使用手动门控来计数PNH细胞。我们评估了一种使用GemStone™（Verity Software House）概率状态模型（PSM）识别GPI缺陷白细胞的自动化方法。
Flow Cytometry is the standard for the detection of glycosylphosphatidylinositol (GPI)‐deficient clones in paroxysmal nocturnal hemoglobinuria (PNH) and related disorders. Although the International Clinical Cytometry Society (ICCS) and the International PNH Interest Group (IPIG) have published guidelines for PNH assays, data analysis has not been standardized. Current analyses use manual gating to enumerate PNH cells. We evaluate an automated approach to identify GPI‐deficient leukocytes using a GemStone™ (Verity Software House) probability state model (PSM).