Rosenberg Carina A, Bill Marie, Rodrigues Matthew A, Hauerslev Mathias, Kerndrup Gitte B, Hokland Peter, Ludvigsen Maja
Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.
Amnis Flow Cytometry, Luminex Corporation, Seattle, Washington, USA.
Cytometry B Clin Cytom. 2021 Sep;100(5):554-567. doi: 10.1002/cyto.b.21975. Epub 2020 Dec 7.
The hallmark of myelodysplastic syndrome (MDS) remains dysplasia in the bone marrow (BM). However, diagnosing MDS may be challenging and subject to inter-observer variability. Thus, there is an unmet need for novel objective, standardized and reproducible methods for evaluating dysplasia. Imaging flow cytometry (IFC) offers combined analyses of phenotypic and image-based morphometric parameters, for example, cell size and nuclearity. Hence, we hypothesized IFC to be a useful tool in MDS diagnostics.
Using a different-from-normal approach, we investigated dyserythropoiesis by quantifying morphometric features in a median of 5953 erythroblasts (range: 489-68,503) from 14 MDS patients, 11 healthy donors, 6 non-MDS controls with increased erythropoiesis, and 6 patients with cytopenia.
First, we morphometrically confirmed normal erythroid maturation, as immunophenotypically defined erythroid precursors could be sequenced by significantly decreasing cell-, nuclear- and cytoplasm area. In MDS samples, we demonstrated cell size enlargement and increased fractions of macronormoblasts in late-stage erythroblasts (both p < .0001). Interestingly, cytopenic controls with high-risk mutational patterns displayed highly aberrant cell size morphometrics. Furthermore, assisted by machine learning algorithms, we reliably identified and enumerated true binucleated erythroblasts at a significantly higher frequency in two out of three erythroblast maturation stages in MDS patients compared to normal BM (both p = .0001).
We demonstrate proof-of-concept results of the applicability of automated IFC-based techniques to study and quantify morphometric changes in dyserythropoietic BM cells. We propose that IFC holds great promise as a powerful and objective tool in the complex setting of MDS diagnostics with the potential for minimizing inter-observer variability.
骨髓增生异常综合征（MDS）的标志仍是骨髓发育异常。然而，诊断MDS可能具有挑战性，且存在观察者间差异。因此，对于评估发育异常的新型客观、标准化和可重复方法存在未满足的需求。成像流式细胞术（IFC）可对表型和基于图像的形态计量学参数进行联合分析，例如细胞大小和核质比。因此，我们推测IFC在MDS诊断中是一种有用的工具。
采用不同于正常的方法，我们通过量化14例MDS患者、11例健康供者、6例红细胞生成增加的非MDS对照和6例血细胞减少症患者中中位数为5953个成红细胞（范围：489 - 68503）的形态计量学特征来研究红细胞生成异常。
首先，我们通过形态计量学证实了正常的红系成熟，因为免疫表型定义的红系前体细胞可通过细胞、细胞核和细胞质面积的显著减小进行排序。在MDS样本中，我们证明了晚期成红细胞中细胞大小增大和巨大幼红细胞比例增加（两者p < 0.0001）。有趣的是，具有高风险突变模式的血细胞减少症对照显示出高度异常的细胞大小形态计量学特征。此外，在机器学习算法的辅助下，我们可靠地识别并计数了MDS患者中三个成红细胞成熟阶段中的两个阶段中真正双核成红细胞的频率明显高于正常骨髓（两者p = 0.0001）。
我们展示了基于IFC的自动化技术用于研究和量化骨髓红细胞生成异常细胞形态计量学变化的概念验证结果。我们提出，在复杂的MDS诊断环境中，IFC作为一种强大而客观的工具具有巨大潜力，有可能最大限度地减少观察者间差异。
