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

10.3791/59324 ◽  
2019 ◽  
Author(s):  
Matthew A. Rodrigues
Keyword(s):  
Flow Cytometry ◽  
Multispectral Imaging ◽  
Micronucleus Assay ◽  
Imaging Flow Cytometry ◽  
Automated Method ◽  
In Vitro Micronucleus Assay

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

2021 ◽  
Author(s):  
John W. Wills ◽  
Jatin R. Verma ◽  
Benjamin J. Rees ◽  
Danielle S. G. Harte ◽  
Qiellor Haxhiraj ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Image Classification ◽  
Micronucleus Assay ◽  
Scoring Method ◽  
Safety Testing ◽  
Imaging Flow Cytometry ◽  
Unseen Data ◽  
In Vitro Micronucleus Assay

AbstractThe 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.

Performance of the flow cytometry-based in vitro micronucleus assay in a validation study

2016 ◽  
Vol 258 ◽  
pp. S316
Author(s):  
D.J. Smart ◽  
F. Helbling ◽  
P. Blandine ◽  
M. Damian ◽  
V. Patrick
Keyword(s):  
Flow Cytometry ◽  
Validation Study ◽  
Micronucleus Assay ◽  
In Vitro Micronucleus Assay

scholarly journals Comparative Study of MSCA-1 and CD146 Isolated Periosteal Cell Subpopulations

2018 ◽  
Vol 51 (3) ◽  
pp. 1193-1206 ◽  
Author(s):  
Felix Umrath ◽  
Carla Thomalla ◽  
Simone Pöschel ◽  
Katja Schenke-Layland ◽  
Siegmar Reinert ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Calcium Phosphate ◽  
Multispectral Imaging ◽  
Osteogenic Potential ◽  
In Vitro Differentiation ◽  
Imaging Flow Cytometry ◽  
Periosteal Cell ◽  
Cell Subpopulations ◽  
Cd146 Expression

Background/Aims: Periosteal tissue is a valuable source of multipotent stem cells for bone tissue engineering. To characterize these cells in detail, we generated an immortalized human cranial periosteal cell line and observed an increased MSCA-1 and CD146 expression, as well as an earlier and stronger mineralization compared to the parental cells. Further, we detected a higher osteogenic potential of MSCA-1high compared to MSCA-1low cranial periosteal cell (CPC) fractions. In the present study, a possible synergism of MSCA-1 and CD146 for periosteal cell mineralization was investigated. Methods: MSCA-1/CD146 positive and negative CPCs were magnetically isolated (MACS) or sorted by flow cytometry (FACS) and subjected to osteogenic differentiation. The expression of osteogenic marker genes in the four subpopulations was analyzed by quantitative real-time PCR. Furthermore, the co-expression of osteogenic markers/antigens was analyzed by multispectral imaging flow cytometry (ImageStream, AMNIS). The mineralization potential was assessed by the quantification of alizarin stainings. Results: While the total cell yield after separation was higher using MACS compared to the FACS approach, the isolation of MSCA-1+/- and CD146+/- subpopulations was more efficient with the FACS separation. The accuracy of the FACS separation of the four distinguished cell subpopulations was confirmed by multispectral imaging flow cytometry. Further, we detected increasing levels of MSCA-1 and CD146 during in vitro differentiation in all subpopulations. However, MSCA-1 expression was significantly higher in the MSCA-1+/CD146+ and MSCA-1+/ CD146- subpopulations, while CD146 expression remained clearly lower in these fractions. Significantly higher gene expression levels of osteogenic markers, ALP and RUNX2, were detected in MSCA-1+ compared to MSCA-1- CPCs at different time points during in vitro differentiation. Staining and quantification of calcium phosphate precipitates revealed a significantly higher mineralization potential of MACS separated MSCA-1+ and CD146- CPCs, compared to their respective counterparts. FACS sorted CPCs displayed earlier mineralization in both MSCA-1+ fractions (d13), while later (d28) only the CD146+/MSCA-1- fraction had a significantly lower calcium phosphate concentration compared to all other fractions. Conclusion: Our results demonstrate, that MSCA-1+ cells isolated from CPCs represent a subpopulation with a higher osteogenic potential. In contrast, we found a lower osteogenic potential in CD146+ CPCs. In conclusion, only MSCA-1, but not CD146, is a suitable marker for the isolation of osteoprogenitors from CPCs.

scholarly journals Enucleation of primitive erythroid cells generates a transient population of “pyrenocytes” in the mammalian fetus

Blood ◽  
2008 ◽  
Vol 111 (4) ◽  
pp. 2409-2417 ◽  
Author(s):  
Kathleen E. McGrath ◽  
Paul D. Kingsley ◽  
Anne D. Koniski ◽  
Rebecca L. Porter ◽  
Timothy P. Bushnell ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Multispectral Imaging ◽  
Fetal Liver ◽  
Fetal Blood ◽  
Immunohistochemical Examination ◽  
Imaging Flow Cytometry ◽  
Transient Population ◽  
Erythroid Maturation ◽  
Definitive Erythropoiesis

Enucleation is the hallmark of erythropoiesis in mammals. Previously, we determined that yolk sac–derived primitive erythroblasts mature in the bloodstream and enucleate between embryonic day (E)14.5 and E16.5 of mouse gestation. While definitive erythroblasts enucleate by nuclear extrusion, generating reticulocytes and small, nucleated cells with a thin rim of cytoplasm (“pyrenocytes”), it is unclear by what mechanism primitive erythroblasts enucleate. Immunohistochemical examination of fetal blood revealed primitive pyrenocytes that were confirmed by multispectral imaging flow cytometry to constitute a distinct, transient cell population. The frequency of primitive erythroblasts was higher in the liver than the bloodstream, suggesting that they enucleate in the liver, a possibility supported by their proximity to liver macrophages and the isolation of erythroblast islands containing primitive erythroblasts. Furthermore, primitive erythroblasts can reconstitute erythroblast islands in vitro by attaching to fetal liver–derived macrophages, an association mediated in part by α4 integrin. Late-stage primitive erythroblasts fail to enucleate in vitro unless cocultured with macrophage cells. Our studies indicate that primitive erythroblasts enucleate by nuclear extrusion to generate erythrocytes and pyrenocytes and suggest this occurs in the fetal liver in association with macrophages. Continued studies comparing primitive and definitive erythropoiesis will lead to an improved understanding of terminal erythroid maturation.

A flow cytometry based in vitro micronucleus assay in TK6 cells-Validation using early stage pharmaceutical development compounds

2010 ◽  
Vol 52 (5) ◽  
pp. 363-372 ◽  
Author(s):  
Magdalena Lukamowicz ◽  
Katherine Woodward ◽  
Micheline Kirsch-Volders ◽  
Willi Suter ◽  
Azeddine Elhajouji
Keyword(s):  
Flow Cytometry ◽  
Early Stage ◽  
Micronucleus Assay ◽  
Pharmaceutical Development ◽  
Tk6 Cells ◽  
In Vitro Micronucleus Assay

scholarly journals Using multispectral imaging flow cytometry to assess an in vitro intracellularBurkholderia thailandensisinfection model

2016 ◽  
Vol 89 (4) ◽  
pp. 328-337 ◽  
Author(s):  
Dominic Jenner ◽  
Catherine Ducker ◽  
Graeme Clark ◽  
Jo Prior ◽  
Caroline A. Rowland
Keyword(s):  
Flow Cytometry ◽  
Multispectral Imaging ◽  
Imaging Flow Cytometry

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

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Matthew A. Rodrigues ◽  
Christine E. Probst ◽  
Artiom Zayats ◽  
Bryan Davidson ◽  
Michael Riedel ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Image Analysis ◽  
Deep Learning ◽  
Micronucleus Assay ◽  
Image Capture ◽  
Imaging Flow Cytometry ◽  
Analysis Strategy ◽  
Regulatory Bodies ◽  
Feature Based

AbstractThe 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.

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