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Ki-67和Bcl-2的门控策略对通过流式细胞术测定非恶性骨髓穿刺液及髓系恶性肿瘤患者穿刺液中增殖和抗凋亡数据的影响。

Impact of the gating strategy for Ki-67 and Bcl-2 on the determination of proliferation and anti-apoptosis data by flow cytometry in non-malignant bone marrow aspirates and aspirates from patients with myeloid malignancies.

作者信息

Mestrum Stefan G C, Vanblarcum Roanalis B Y, de Wit Norbert C J, Drent Roosmarie J M, Boonen Bert T, van Hemert Wouter L W, Hopman Anton H N, Ramaekers Frans C S, Leers Math P G

机构信息

Department of Molecular Cell Biology, GROW-School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, the Netherlands.

Department of Clinical Chemistry & Hematology, Zuyderland Medical Center, Sittard-Geleen, the Netherlands.

出版信息

Data Brief. 2023 Jun 9;49:109284. doi: 10.1016/j.dib.2023.109284. eCollection 2023 Aug.

Abstract

This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferative and anti-apoptotic activity in hematopoietic cells. This dataset includes analyses of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell populations in non-malignant BM, and in BM disorders, i.e. myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. Because gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 27 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research into the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations were identified based on cell biological characteristics, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells in the identification of minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology.

摘要

这篇《数据简报》文章展示了一种流式细胞术检测方法,该方法用于获取和分析造血细胞中的增殖活性和抗凋亡活性。该数据集包括对非恶性骨髓、骨髓疾病(即骨髓增生异常综合征(MDS)和急性髓系白血病(AML))中不同髓系骨髓(BM)细胞群体的Ki-67阳性分数(Ki-67增殖指数)和Bcl-2阳性分数(Bcl-2抗凋亡指数)的分析。本数据集包括:1)CD34阳性原始细胞、红系细胞、髓系细胞和单核细胞的百分比,以及2)这些细胞群体以表格形式呈现的测定的Ki-67阳性分数和Bcl-2阳性分数。这使得在不同环境下重复这些分析时能够对数据进行比较和再现。由于对Ki-67阳性和Bcl-2阳性细胞进行设门是该检测中的关键步骤,因此比较了不同的设门方法以确定最敏感和特异的方法。对50例非恶性、25例MDS和27例AML病例的骨髓穿刺液中的BM细胞用7种不同的抗体组合进行染色,并进行流式细胞术检测,以确定不同髓系细胞群体的Ki-67阳性细胞和Bcl-2阳性细胞。然后将Ki-67或Bcl-2阳性细胞除以相应细胞群体的细胞总数,以生成Ki-67阳性分数(Ki-67增殖指数)或Bcl-2阳性分数(Bcl-2抗凋亡指数)。所呈现的数据可能有助于在其他实验室中建立和标准化对非恶性BM以及MDS和AML患者中不同髓系细胞群体的Ki-67增殖指数和Bcl-2抗凋亡指数的流式细胞术分析。对Ki-67阳性分数和Bcl-2阳性分数进行正确设门的指导对于不同实验室之间实现标准化至关重要。此外,这些数据和所呈现的检测方法允许在研究和临床环境中应用Ki-67和Bcl-2,并且这种方法可以作为优化设门策略以及随后对除增殖和抗凋亡之外的其他细胞生物学过程进行研究的基础。这些数据还可以促进未来对这些参数在髓系恶性肿瘤诊断、髓系恶性肿瘤预后以及这些恶性肿瘤对抗癌治疗的耐药性方面作用的研究。由于根据细胞生物学特征鉴定了特定群体,这些数据通过用这些恶性肿瘤各自的增殖和抗凋亡特征确认结果(例如MDS或AML诊断),可总体上用于评估流式细胞术中的设门算法。Ki-67增殖指数和Bcl-2抗凋亡指数可能潜在地用于基于监督机器学习算法对MDS和AML进行分类,而无监督机器学习可在单细胞水平上部署,以在识别微小残留病时潜在地区分非恶性细胞和恶性细胞。因此,本数据集可能会引起内科血液学家、对血液肿瘤学有兴趣的免疫学家、血液学亚专业的临床化学家以及血液肿瘤学领域的研究人员的关注。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b39/10319170/061df24b05d0/gr1.jpg

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