Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine‐learning assisted morphometrics

Abstract

Background 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. Methods 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. Results 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). Conclusion 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.

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

“…Current MDS diagnosis routines are under reconsideration due to reproducibility issues, high labor intensiveness, and requirement of expert staff 2 , 4 , 19 . These issues could be addressed utilizing a combination of imaging flow cytometry (IFC) for high-throughput acquisition and machine learning for automated data analysis 20 , 21 . In IFC, fluorescence images are captured which allows labelling of different cell types and intracellular structures.…”

Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes

“…Although BM evaluation is a conditio sine qua non for the definitive diagnosis of MDS, few studies previously employed ML for the detection and morphological characterization of dysplastic cells in BM smears [ 55 , 56 , 57 , 58 , 59 ]. The identification of BM dysplasia for establishing an MDS diagnosis may be challenging because of the presence of many types of progenitor cells at different stages of maturation and the absence of specific pathognomonic features.…”

“…Alternatively, ML-based imaging flow cytometry (IFC) can be used to detect BM dyserythropoiesis by identifying and quantifying morphometric aberrancies in erythroid precursors [ 59 ]. One of the features of dyserythropoiesis in MDS is the presence of enlarged cells with normal cytoplasmic/nuclear maturation profile, also known as macronormoblasts [ 61 ].…”

“…One of the features of dyserythropoiesis in MDS is the presence of enlarged cells with normal cytoplasmic/nuclear maturation profile, also known as macronormoblasts [ 61 ]. IFC detects macronormoblastic changes while enhancing the recognition of binucleated events through its ability to process thousands of cells along with ML’s decision-making accuracy [ 59 ]. Rosenberg et al demonstrated this by quantifying morphometric changes in a median of 5953 erythroblasts (range 489–68,503) from 14 MDS patients, 11 healthy controls, 6 non-MDS patients with increased erythropoiesis (e.g., megaloblastic anemia due to vitamin B12 deficiency), and 6 patients with other-causes cytopenia [ 59 ].…”

Issue Highlights—September 2021

“…Another application of ImageStream X is proposed by Rosenberg et al (2021) focusing on dyserythropoiesis. Here the technology applied and the strategies developed for an in‐depth analysis of the erythroid compartment are again well detailed and thoroughly explained.…”