Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.

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.

Application of the Cytokinesis-Block Micronucleus Assay for High-Dose Exposures Using Imaging Flow Cytometry.

The cytokinesis-block micronucleus assay is a well-established method to assess radiation-induced genetic damage in human cells. This assay has been adapted to imaging flow cytometry (IFC), allowing automated analysis of many cells, and eliminating the need to create microscope slides. Furthermore, to improve the efficiency of assay performance, a small-volume method previously developed was employed. Irradiated human blood samples were cultured, stained, and analyzed by IFC to produce images of the cells. Samples were run using both manual and 96-well plate automated acquisition. Multiple parameter-based image features were collected for each sample, and the results were compared to confirm that these acquisition methods are functionally identical. This paper details the multi-parametric analysis developed and the resulting calibration curves up to 10 Gy. The calibration curves were created using a quadratic random coefficient model with Poisson errors, as well as a logistic discriminant function. The curves were then validated with blinded, irradiated samples, using relative bias and relative mean square error. Overall, the accuracy of the dose estimates was adequate for triage dosimetry (within 1 Gy of the true dose) over 90% of the time for lower doses and about half the time for higher doses, with the lowest success rate between 5 and 6 Gy where the calibration curve reached its peak and there was the smallest change in MN/BNC with dose. This work describes the application of a novel multi-parametric analysis that fits the calibration curves and allows dose estimates up to 10 Gy, which were previously limited to 4 Gy. Furthermore, it demonstrates that the results from samples acquired manually and with the autosampler are functionally similar.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 µg/mL) and/or carbendazim (0.8-1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

Automation of the in vitro micronucleus assay using the Imagestream® imaging flow cytometer.

The in vitro micronucleus (MN) assay is a well-established test for evaluating genotoxicity and cytotoxicity. The use of manual microscopy to perform the assay can be laborious and often suffers from user subjectivity and interscorer variability. Automated methods including slide-scanning microscopy and conventional flow cytometry have been developed to eliminate scorer bias and improve throughput. However, these methods possess several limitations such as lack of cytoplasmic visualization using slide-scanning microscopy and the inability to visually confirm the legitimacy of MN or storage of image data for re-evaluation using flow cytometry. The ImageStreamX® MK II (ISX) imaging flow cytometer has been demonstrated to overcome all of these limitations. The ISX combines the speed, statistical robustness, and rare event capture capability of conventional flow cytometry with high resolution fluorescent imagery of microscopy and possesses the ability to store all collected image data. This paper details the methodology developed to perform the in vitro MN assay in human lymphoblastoid TK6 cells on the ISX. High resolution images of micronucleated mono- and bi-nucleated cells as well as polynucleated cells can be acquired at a high rate of capture. All images can then be automatically identified, categorized and enumerated in the data analysis software that accompanies the ImageStream, allowing for the scoring of both genotoxicity and cytotoxicity. The results demonstrate that statistically significant increases in MN frequency when compared with solvent controls can be detected at varying levels of cytotoxicity following exposure to well-known aneugens and clastogens. This work demonstrates a fully automated method for performing the in vitro micronucleus assay on the ISX imaging flow cytometry platform. © 2018 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.

Foundations of identifying individual chromosomes by imaging flow cytometry with applications in radiation biodosimetry.

Biodosimetry is an important tool for triage in the case of large-scale radiological or nuclear emergencies, but traditional microscope-based methods can be tedious and prone to scorer fatigue. While the dicentric chromosome assay (DCA) has been adapted for use in triage situations, it is still time-consuming to create and score slides. Recent adaptations of traditional biodosimetry assays to imaging flow cytometry (IFC) methods have dramatically increased throughput. Additionally, recent improvements in image analysis algorithms in the IFC software have resulted in improved specificity for spot counting of small events. In the IFC method for the dicentric chromosome analysis (FDCA), lymphocytes isolated from whole blood samples are cultured with PHA and Colcemid. After incubation, lymphocytes are treated with a hypotonic solution and chromosomes are isolated in suspension, labelled with a centromere marker and stained for DNA content with DRAQ5. Stained individual chromosomes are analyzed on the ImageStream®X (EMD-Millipore, Billerica, MA) and mono- and dicentric chromosome populations are identified and enumerated using advanced image processing techniques. Both the preparation of the isolated chromosome suspensions as well as the image analysis methods were fine-tuned in order to optimize the FDCA. In this paper we describe the method to identify and score centromeres in individual chromosomes by IFC and show that the FDCA method may further improve throughput for triage biodosimetry in the case of large-scale radiological or nuclear emergencies.

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.

Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.

Pyroptosis Markers in Human Primary Specimens: Quantification of Intracellular ASC Specks by Imaging Flow Cytometry and Extracellular Oxidized Mitochondrial by ELISA.

Pyroptosis is an immunological response to infection and cellular stresses initiated by inflammasome oligomerization resulting in the release of pro-inflammatory factors including cytokines and other immune stimuli into the extracellular matrix. In order to understand the role of inflammasome activation and subsequent pyroptosis in human infection and disease pathogenesis and to explore markers of these signaling events as potential disease or response biomarkers, we must utilize quantitative, reliable, and reproducible assays to readily investigate these pathways in primary specimens. Here, we describe two methods using imaging flow cytometry for evaluation of inflammasome ASC specks in homogeneous peripheral blood monocytes and in bulk, heterogeneous peripheral blood mononuclear cells. Both methods can be applied to assess speck formation as a biomarker for inflammasome activation in primary specimens. Additionally, we describe the methods for quantification of extracellular oxidized mitochondrial DNA from primary plasma samples, serving as a proxy for pyroptosis. Collectively, these assays may be utilized to determine pyroptotic influences on viral infection and disease development or as diagnostic aids and response biomarkers.

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence.

The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by scoring MN in once-divided, cytokinesis-blocked binucleated cells or fully divided mononucleated cells. Historically, light microscopy has been the gold standard method to score the assay, but it is laborious and subjective. Flow cytometry has been used in recent years to score the assay, but is limited by the inability to visually confirm key aspects of cellular imagery. Imaging flow cytometry (IFC) combines high-throughput image capture and automated image analysis, and has been successfully applied to rapidly acquire imagery of and score all key events in the MN assay. Recently, it has been demonstrated that artificial intelligence (AI) methods based on convolutional neural networks can be used to score MN assay data acquired by IFC. This paper describes all steps to use AI software to create a deep learning model to score all key events and to apply this model to automatically score additional data. Results from the AI deep learning model compare well to manual microscopy, therefore enabling fully automated scoring of the MN assay by combining IFC and AI.

Somatic gene mutations expose cytoplasmic DNA to co-opt the cGAS/STING/NLRP3 axis in myelodysplastic syndromes.

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.

The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation.

The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of the assay, including publication of a standard protocol and extensive validation. However, the diverse morphology of skin cells makes cell preparation and scoring of micronuclei (MN) tedious and subjective, thus requiring a high level of technical expertise for evaluation. This ultimately has a negative impact on throughput and the assay would benefit by the development of an automated method which could reduce scoring subjectivity while also improving the robustness of the assay by increasing the number of cells that can be scored. Imaging flow cytometry (IFC) with the ImageStream®X Mk II can capture high-resolution transmission and fluorescent imagery of cells in suspension. This proof-of-principle study describes protocol modifications that enable such automated measurement in 3D skin cells following exposure to mitomycin C and colchicine. IFC was then used for automated image capture and the Amnis® Artificial Intelligence (AAI) software permitted identification of binucleated (BN) cells with 91% precision. On average, three times as many BN cells from control samples were evaluated using IFC compared to the standard manual analysis. When IFC MNBN cells were visually scored from within the BN cell images, their frequency compared well with manual slide scoring, showing that IFC technology can be applied to the RSMN assay. This method enables faster time to result than microscope-based scoring and the initial studies presented here demonstrate its capability for the detection of statistically significant increases in MNBN frequencies. This work therefore demonstrates the feasibility of combining IFC and AAI to automate scoring for the RSMN assay and to improve its throughput and statistical robustness.

The in vitro micronucleus assay using imaging flow cytometry and deep learning.

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.

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

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.

Development of the FAST-DOSE assay system for high-throughput biodosimetry and radiation triage.

Following a large-scale radiological incident, there is a need for FDA-approved biodosimetry devices and biomarkers with the ability to rapidly determine past radiation exposure with sufficient accuracy for early population triage and medical management. Towards this goal, we have developed FAST-DOSE (Fluorescent Automated Screening Tool for Dosimetry), an immunofluorescent, biomarker-based system designed to reconstruct absorbed radiation dose in peripheral blood samples collected from potentially exposed individuals. The objective of this study was to examine the performance of the FAST-DOSE assay system to quantify intracellular protein changes in blood leukocytes for early biodosimetry triage from humanized NOD-scid-gamma (Hu-NSG) mice and non-human primates (NHPs) exposed to ionizing radiation up to 8 days after radiation exposure. In the Hu-NSG mice studies, the FAST-DOSE biomarker panel was able to generate delivered dose estimates at days 1, 2 and 3 post exposure, whereas in the NHP studies, the biomarker panel was able to successfully classify samples by dose categories below or above 2 Gy up to 8 days after total body exposure. These results suggest that the FAST-DOSE bioassay has large potential as a useful diagnostic tool for rapid and reliable screening of potentially exposed individuals to aid early triage decisions within the first week post-exposure.

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry.

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.

Automated Triage Radiation Biodosimetry: Integrating Imaging Flow Cytometry with High-Throughput Robotics to Perform the Cytokinesis-Block Micronucleus Assay.

The cytokinesis-block micronucleus (CBMN) assay has become a fully-validated and standardized method for radiation biodosimetry. The assay is typically performed using microscopy, which is labor intensive, time consuming and impractical after a large-scale radiological/nuclear event. Imaging flow cytometry (IFC), which combines the statistical power of traditional flow cytometry with the sensitivity and specificity of microscopy, has been recently used to perform the CBMN assay. Since this technology is capable of automated sample acquisition and multi-file analysis, we have integrated IFC into our Rapid Automated Biodosimetry Technology (RABiT-II). Assay development and optimization studies were designed to increase the yield of binucleated cells (BNCs), and improve data acquisition and analysis templates to increase the speed and accuracy of image analysis. Human peripheral blood samples were exposed ex vivo with up to 4 Gy of c rays at a dose rate of 0.73 Gy/min. After irradiation, samples were transferred to microtubes (total volume of 1 ml including blood and media) and organized into a standard 8 × 12 plate format. Sample processing methods were modified by increasing the blood-to-media ratio, adding hypotonic solution prior to cell fixation and optimizing nuclear DRAQ5 staining, leading to an increase of 81% in BNC yield. Modification of the imaging processing algorithms within IFC software also improved BNC and MN identification, and reduced the average time of image analysis by 78%. Finally, 50 ll of irradiated whole blood was cultured with 200 ll of media in 96-well plates. All sample processing steps were performed automatically using the RABiT-II cell: :explorer robotic system adopting the optimized IFC-CBMN assay protocol. The results presented here detail a novel, high-throughput RABiT-IFC CBMN assay that possesses the potential to increase capacity for triage biodosimetry during a large-scale radiological/nuclear event.

The potential for complete automated scoring of the cytokinesis block micronucleus cytome assay using imaging flow cytometry.

The lymphocyte Cytokinesis-Block Micronucleus (CBMN) assay was originally developed for the measurement of micronuclei (MN) exclusively in binucleated (BN) cells, which represent the population of cells that can express MN because they completed nuclear division. Recently the assay has evolved into a comprehensive cytome method to include biomarkers that measure chromosomal instability and cytotoxicity by quantification of nuclear buds (NBUDs), nucleoplasmic bridges (NPBs) and apoptotic/necrotic cells. Furthermore, enumeration of mono- and polynucleated cells allows for computation of the nuclear division index (NDI) to assess mitotic activity. Typically performed by manual microscopy, the CBMN cytome assay is laborious and subject to scorer bias and fatigue, leading to inter- and intra-scorer variability. Automated microscopy and conventional flow cytometry methods have been developed to automate scoring of the traditional and cytome versions of the assay. However, these methods have several limitations including the requirement to create high-quality microscope slides, lack of staining consistency and sub-optimal nuclear/cytoplasmic visualization. In the case of flow cytometry, stripping of the cytoplasmic membrane makes it impossible to measure MN in BN cells, calculate the NDI or to quantify apoptotic or necrotic cells. Moreover, the absence of cellular visualization using conventional flow cytometry, makes it impossible to quantify NBUDs and NPBs. In this review, we propose that imaging flow cytometry (IFC), which combines high resolution microscopy with flow cytometry, may overcome these limitations. We demonstrate that by using IFC, images from cells in suspension can be captured, removing the need for microscope slides and allowing visualization of intact cytoplasmic membranes and DNA content. Thus, mono-, bi- and polynucleated cells with and without MN can be rapidly and automatically identified and quantified. Finally, we present high-resolution cell images containing NBUDs and NPBs, illustrating that IFC possesses the potential for completely automated scoring of all components of the CBMN cytome assay.

The Application of Imaging Flow Cytometry to High-Throughput Biodosimetry.

Biodosimetry methods, including the dicentric chromosome assay, the cytokinesis-block micronucleus assay and the γH2AX marker of DNA damage are used to determine the dose of ionizing radiation. These techniques are particularly useful when physical dosimetry is absent or questioned. While these assays can be very sensitive and specific, the standard methods need to be adapted to increase sample throughput in the case of a large-scale radiological/nuclear event. Recent modifications to the microscope-based assays have resulted in some increased throughput, and a number of biodosimetry networks have been, and continue to be, established and strengthened. As the imaging flow cytometer (IFC) is a technology that can automatically image and analyze processed blood samples for markers of radiation damage, the microscope-based biodosimetry techniques can be modified for the IFC for high-throughput biological dosimetry. Furthermore, the analysis templates can be easily shared between networked biodosimetry laboratories for increased capacity and improved standardization. This review describes recent advances in IFC methodology and their application to biodosimetry.

Validation of the Cytokinesis-block Micronucleus Assay Using Imaging Flow Cytometry for High Throughput Radiation Biodosimetry.

The cytokinesis-block micronucleus assay can be employed in triage radiation biodosimetry to determine the dose of radiation to an exposed individual by quantifying the frequency of micronuclei in binucleated lymphocyte cells. Partially automated analysis of the assay has been applied to traditional microscope-based methods, and most recently, the assay has been adapted to an automated imaging flow cytometry method. This method is able to automatically score a larger number of binucleated cells than are typically scored by microscopy. Whole blood samples were irradiated, divided into 2 mL and 200 µL aliquots, cultured for 48 h and 72 h, and processed to generate calibration curves from 0-4 Gy. To validate the method for use in radiation biodosimetry, nine separate whole blood samples were then irradiated to known doses, blinded, and processed. Results indicate that dose estimations can be determined to within ±0.5 Gy of the delivered dose after only 48 h of culture time with an initial blood volume of 200 µL. By performing the cytokinesis-block micronucleus assay using imaging flow cytometry, a significant reduction in the culture time and volume requirements is possible, which greatly increases the applicability of the assay in high throughput triage radiation biodosimetry.