Fedorov Kateryna, Barouqa Mohammad, Yin David, Kushnir Margarita, Billett Henny H, Reyes Gil Morayma
Division of Hematology, Albert Einstein College of Medicine, Bronx, NY 10467, USA.
Division of Hematology, Department of Oncology, Montefiore Medical Center, Albert Einstein School of Medicine, 3411 Wayne Ave, Ground Floor, Bronx, NY 10467, USA.
Life (Basel). 2023 Feb 23;13(3):623. doi: 10.3390/life13030623.
Neutrophil Extracellular Traps (NETs) are large neutrophil-derived structures composed of decondensed chromatin, cytosolic, and granule proteins. NETs play an important role in fighting infection, inflammation, thrombosis, and tumor progression processes, yet their fast and reliable identification has been challenging. Smudge cells (SCs) are a subcategory of white cells identified by CellaVision, a hematology autoanalyzer routinely used in clinical practice that uses digital imaging to generate "manual" differentials of peripheral blood smears. We hypothesize that a proportion of cells identified in the SC category by CellaVision Hematology Autoanalyzers are actually NETs. We demonstrate that NET-like SCs are not present in normal blood samples, nor are they an artifact of smear preparation. NET-like SCs stain positive for neutrophil markers such as myeloperoxidase, leukocyte alkaline phosphatase, and neutrophil elastase. On flow cytometry, cells from samples with high percent NET-like SCs that are positive for surface DNA are also positive for CD45, myeloperoxidase and markers of neutrophil activation and CD66b. Samples with NET-like SCs have a strong side fluorescent (SFL) signal on the white count and nucleated red cells (WNR) scattergram, representing cells with high nucleic acid content. When compared to patients with low percent SCs, those with a high percentage of SCs have a significantly higher incidence of documented bacterial and viral infections. The current methodology of NET identification is time-consuming, complicated, and cumbersome. In this study, we present data supporting identification of NETs by CellaVision, allowing for easy, fast, cost-effective, and high throughput identification of NETs that is available in real time and may serve as a positive marker for a bacterial or viral infections.
中性粒细胞胞外陷阱（NETs）是由解聚的染色质、细胞溶质和颗粒蛋白组成的大型中性粒细胞衍生结构。NETs在对抗感染、炎症、血栓形成和肿瘤进展过程中发挥着重要作用，然而其快速可靠的识别一直具有挑战性。涂抹细胞（SCs）是CellaVision识别的白细胞亚类，CellaVision是临床实践中常用的血液学自动分析仪，它使用数字成像来生成外周血涂片的“手工”分类。我们假设，CellaVision血液学自动分析仪在SCs类别中识别出的一部分细胞实际上是NETs。我们证明，NET样SCs在正常血液样本中不存在，也不是涂片制备的假象。NET样SCs对中性粒细胞标志物如髓过氧化物酶、白细胞碱性磷酸酶和中性粒细胞弹性蛋白酶呈阳性染色。在流式细胞仪上，来自NET样SCs百分比高且表面DNA呈阳性的样本中的细胞，对CD45、髓过氧化物酶以及中性粒细胞活化标志物和CD66b也呈阳性。含有NET样SCs的样本在白细胞计数和有核红细胞（WNR）散点图上有很强的侧向荧光（SFL）信号，代表核酸含量高的细胞。与SCs百分比低的患者相比，SCs百分比高的患者记录在案的细菌和病毒感染发生率显著更高。目前NET识别的方法耗时、复杂且繁琐。在本研究中，我们提供的数据支持通过CellaVision识别NETs，从而能够轻松、快速、经济高效且高通量地实时识别NETs，并且NETs可能作为细菌或病毒感染的阳性标志物。
