人嗜碱性细胞表达CD22而不表达CD19。

K Han, Y Kim, J Lee, J Lim, K Y Lee, C S Kang, W I Kim, B K Kim, S I Shim, S M Kim
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引用次数: 0

摘要

背景:即使是现代的自动细胞计数器也不能精确地计数嗜碱性细胞。因此,我们需要一种快速、准确、精密、简便的嗜碱性粒细胞计数方法。方法:采用流式细胞术计数嗜碱性细胞(CD22+/CD19-)和B细胞(CD22+/CD19+)。在一个大的淋巴细胞光散射门内,从总数中测定%嗜碱性细胞(G%baso)和%B细胞(G%B)。另一种分析方法是制作两个区域(R1为嗜碱性细胞区,R2为B细胞区),不加门控地测定其中的%嗜碱性细胞区(R1%baso)和%B细胞区(R2%B)。对21例正常人和43例慢性粒细胞白血病(CML)患者进行了流式细胞术嗜碱性粒细胞计数(Ma%baso)和库尔特电子细胞计数(Hialeah, FL) (Auto%baso)的比较。使用FACSCalibur (Becton Dickinson, San Jose, CA)对CD22+/CD19-细胞进行分类。结果:所有样品的G%baso为4.66 +/- 5.35%,R1%baso为4.23 +/- 4.88%,两者相关性良好(r = 0.996, P < 0.001)。所有样本的G%B为1.55 +/- 1.68%,R2%B为1.59 +/- 1.67%,两者也具有良好的相关性(r = 0.993, P < 0.001)。其相关性在正常对照中优于CML。G%baso与Ma%baso (r = 0.827)、Auto%baso (r = 0.806)相关良好,R1%baso与Ma%baso (r = 0.831)相关良好,与Auto%baso相关性较差(r = 0.734)。Auto%baso与Ma%baso相关性最差(r = 0.692)。CD22+/CD19-细胞均为嗜碱性细胞(99.48 +/- 0.30%),表达CD13、CD33和弱CD45,未检测到CD3、CD14、CD16和HLA-DR。结论:我们发现了一种特异性的标记组合来鉴定嗜碱性粒细胞(CD22+/CD19-),我们认为使用这些标记的流式细胞术分析是一种简单、可靠和准确的嗜碱性粒细胞计数方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human basophils express CD22 without expression of CD19.

Background: Even modern automatic cell counters cannot count basophils precisely. Therefore, we need a rapid, accurate, precise, and easy method for counting basophils.

Methods: Using flow cytometry, basophils (CD22+/CD19-) and B cells (CD22+/CD19+) were counted. Within a large lymphocyte light scatter gate, % basophils (G%baso) and % B cells (G%B) were determined from the total count. Another method of analysis was to make two regions (R1 for basophils and R2 for B cells) and to determine in those the % basophils (R1%baso) and % B cells (R2%B) without gating. The flow cytometric basophil counts of the blood of 21 normal controls and 43 chronic myelogenous leukemia (CML) patients were compared with manual basophil count (Ma%baso) and basophil count by Coulter electronic cell counter (Hialeah, FL) (Auto%baso). CD22+/CD19- cells were sorted by a FACSCalibur (Becton Dickinson, San Jose, CA).

Results: The G%baso of all samples was 4.66 +/- 5.35%, and R1%baso was 4.23 +/- 4.88%, and they were well-correlated (r = 0.996, P < 0.001). The G%B of all samples was 1.55 +/- 1.68%, and R2%B was 1.59 +/- 1.67%, and they were also well-correlated (r = 0.993, P < 0.001). Their correlation was better in normal controls than in CML. G%baso was well-correlated to Ma%baso (r = 0.827) and Auto%baso (r = 0.806), and R1%baso was well-correlated to Ma%baso (r = 0.831) but showed poor correlation to Auto%baso (r = 0.734). Auto%baso revealed the poorest correlation to Ma%baso (r = 0.692). The sorted CD22+/CD19- cells were all basophils (99.48 +/- 0.30%), and they revealed CD13, CD33, and dim CD45 expression, whereas CD3, CD14, CD16, and HLA-DR were not detected on them.

Conclusions: We discovered a specific marker combination to identify basophils (CD22+/CD19-), and we suggest that flow cytometric analysis using these markers is an easy, reliable, and accurate method of basophil counting.

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