Automation of the in vitro micronucleus assay using the Imagestream<sup>®</sup> imaging flow cytometer

Rodrigues, Matthew A.

doi:10.1002/cyto.a.23493

Cited by 30 publications

(51 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, these approaches require that cells are exposed to a test chemical and grown to provide sufficient time for chromosomal damage and/or cell cycle proliferation defects to manifest, which are required for micronucleus formation. Many of these approaches involve microscopy, flow cytometry, or image-based flow cytometry and include the use of DNA counterstains (Giemsa or fluorescent DNA dyes), fluorescence in situ hybridization (FISH) probes (chromosome enumeration probes), or kinetochore antibodies to identify micronuclei and/or their contents [48][49][50][51][52][53][54]. As an alternative, our scQuantIM approach does not depend on the efficient inhibition of cytokinesis by cytochalasin B, a compound that by itself has been shown to induce genome instability [34,55] and could conceivably synergize with test compounds or gene silencing to exacerbate micronucleus formation.…”

Section: Discussionmentioning

confidence: 99%

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

Lepage

Thompson

Larson

et al. 2020

Cells

View full text Add to dashboard Cite

Micronuclei are small, extranuclear bodies that are distinct from the primary cell nucleus. Micronucleus formation is an aberrant event that suggests a history of genotoxic stress or chromosome mis-segregation events. Accordingly, assays evaluating micronucleus formation serve as useful tools within the fields of toxicology and oncology. Here, we describe a novel micronucleus formation assay that utilizes a high-throughput imaging platform and automated image analysis software for accurate detection and rapid quantification of micronuclei at the single cell level. We show that our image analysis parameters are capable of identifying dose-dependent increases in micronucleus formation within three distinct cell lines following treatment with two established genotoxic agents, etoposide or bleomycin. We further show that this assay detects micronuclei induced through silencing of the established chromosome instability gene, SMC1A. Thus, the micronucleus formation assay described here is a versatile and efficient alternative to more laborious cytological approaches, and greatly increases throughput, which will be particularly beneficial for large-scale chemical or genetic screens.

show abstract

Section: Discussionmentioning

confidence: 99%

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

Lepage

Thompson

Larson

et al. 2020

Cells

View full text Add to dashboard Cite

show abstract

“…Recently, the CBMN assay has been adapted to an IFCbased method for use in genetic toxicology testing (19,20) and triage radiation biodosimetry (21)(22)(23)(24)(25). These methods demonstrated that cellular images of both the cytoplasm and fluorescence-labeled nuclei and MN could be captured at higher throughput than other methods used to perform the assay.…”

Section: Introductionmentioning

confidence: 99%

“…BNCs and MN were automatically identified in the Image Data Exploration and Analysis Software (IDEASt) by using mathematical algorithms to implement BNC/MN scoring criteria (2,26). The IFC-based version of the CBMN assay has been shown to automatically score more BNCs than can be scored using manual microscopy in a fraction of the time, which enhances the statistical robustness and speed of the assay (19). For radiation biodosimetry, dose-response calibration curves from whole blood samples exposed to 0-4 Gy of X rays were created to quantify the frequency of MN per BNC (24).…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Rodrigues²,

Repin³

et al. 2019

Radiation Research

Self Cite

View full text Add to dashboard Cite

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 3 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.

show abstract

“…Furthermore, it has also been applied to replace the use of manual microscopy for the in vitro micronucleus assay used to study geno-and cytotoxicity. The use of IFC allowed for more automation and informative visualizations (3). IFC has also been used to study the partitioning of molecules across the plane of cell division in a statistically robust manner (4,5,6).…”

Section: Introductionmentioning

confidence: 99%

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Lippeveld

Knill

Ladlow

et al. 2019

Preprint

View full text Add to dashboard Cite

Imaging flow cytometry (IFC) produces up to 12 different information-rich images of single cells at a throughput of 5000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks. Firstly, it is hard to reproduce. Secondly, it is subjective and biased. And thirdly, it is time-consuming for large experiments. Therefore, it would be advantageous to replace manual gating with an automated process, which could be based on stain-free measurements originating from the brightfield and darkfield image channels.To realise this potential, advanced data analysis methods are required, in particular, machine learning. Previous works have successfully tested this approach on cell cycle phase classification with both a classical machine learning approach based on manually engineered features, and a deep learning approach. In this work, we compare both approaches extensively on the complex problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream-X MK II imaging flow cytometer. Two samples were stained for the identification of 8 white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both datasets, four machine learning classifiers were evaluated on stain-free imagery using stratified 5-fold cross-validation. On the white blood cell dataset the best obtained results were 0.776 and 0.697 balanced accuracy for classical machine learning and deep learning, respectively. On the eosinophil dataset this was 0.866 and 0.867 balanced accuracy. From the experiments we conclude that classifying distinct cell types based on only stain-free images is possible with these techniques. However, both approaches did not always succeed in making reliable cell subtype classifications. Also, depending on the cell type, we find that even though the deep learning approach requires less expert input, it performs on par with a classical approach.

show abstract

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

Cited by 30 publications

References 60 publications

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

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

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Contact Info

Product

Resources

About

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

Cited by 30 publications

References 60 publications

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

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

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Contact Info

Product

Resources

About

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