The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream^® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS^® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis^® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream^® compares well to manual microscopy and outperforms IDEAS^® feature-based analysis, facilitating full automation of the MN assay.

NLRP3 inflammasome and IFN-stimulated gene (ISG) induction are key biological drivers of ineffective hematopoiesis and inflammation in myelodysplastic syndromes (MDSs). Gene mutations involving mRNA splicing and epigenetic regulatory pathways induce inflammasome activation and myeloid lineage skewing in MDSs through undefined mechanisms. Using immortalized murine hematopoietic stem and progenitor cells harboring these somatic gene mutations and primary MDS BM specimens, we showed accumulation of unresolved R-loops and micronuclei with concurrent activation of the cytosolic sensor cyclic GMP-AMP synthase. Cyclic GMP-AMP synthase/stimulator of IFN genes (cGAS/STING) signaling caused ISG induction, NLRP3 inflammasome activation, and maturation of the effector protease caspase-1. Deregulation of RNA polymerase III drove cytosolic R-loop generation, which upon inhibition, extinguished ISG and inflammasome response. Mechanistically, caspase-1 degraded the master erythroid transcription factor, GATA binding protein 1, provoking anemia and myeloid lineage bias that was reversed by cGAS inhibition in vitro and in Tet2 ^–/– hematopoietic stem and progenitor cell–transplanted mice. Together, these data identified a mechanism by which functionally distinct mutations converged upon the cGAS/STING/NLRP3 axis in MDS, directing ISG induction, pyroptosis, and myeloid lineage skewing.

Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.

The in vitro micronucleus (MN) assay is often used to evaluate cytotoxicity and genotoxicity but scoring the assay via manual microscopy is laborious and introduces uncertainty in results due to variability between scorers. To remedy this, automated slide-scanning microscopy as well as conventional flow cytometry methods have been introduced in an attempt to remove scorer bias and improve throughput. However, these methods have their own inherent limitations such as inability to visualize the cytoplasm of the cell and the lack of visual MN verification or image data storage with flow cytometry. Multispectral Imaging Flow Cytometry (MIFC) has the potential to overcome these limitations. MIFC combines the high resolution fluorescent imagery of microscopy with the statistical robustness and speed of conventional flow cytometry. In addition, all collected imagery can be stored in dose-specific files. This paper describes the protocol developed to perform a fully automated version of the MN assay on MIFC. Human lymphoblastoid TK6 cells were enlarged using a hypotonic solution (75 mM KCl), fixed with 4% formalin and the nuclear content was stained with Hoechst 33342. All samples were run in suspension on the MIFC, permitting acquisition of high resolution images of all key events required for the assay (e.g. binucleated cells with and without MN as well as mononucleated and polynucleated cells). Images were automatically identified, categorized and enumerated in the MIFC data analysis software, allowing for automated scoring of both cytotoxicity and genotoxicity. Results demonstrate that using MIFC to perform the in vitro MN assay allows statistically significant increases in MN frequency to be detected at several different levels of cytotoxicity when compared to solvent controls following exposure of TK6 cells to Mitomycin C and Colchicine, and that no significant increases in MN frequency are observed following exposure to Mannitol.

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