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.

ImageStream promyelocytic leukemia protein immunolocalization: in search of promyelocytic leukemia cells.

Acute promyelocytic leukemia (APL) is a hematological emergency in which a rapid diagnosis is essential for early administration of appropriate therapy, including all-trans retinoic acid before the onset of fatal coagulopathy. Currently, the following methodologies are widely used for rapid initial diagnosis of APL: 1) identification of hypergranular leukemic promyelocytes by using classical morphology; 2) identification of cells with diffuse promyelocytic leukemia (PML) protein distribution by immunofluorescence microscopy; 3) evidence of aberrant promyelocyte surface immunophenotype by conventional flow cytometry (FCM). Here, we show a method for immunofluorescent detection of PML localization using ImageStream FCM. This technique provides objective per-cell quantitative image analysis for statistically large sample sizes, enabling precise and operator-independent PML pattern recognition even in electronic and real dilution experiments up to 10% of APL cellular presence. Therefore, we evidence that this method could be helpful for rapid and objective initial diagnosis and the prompt initiation of APL treatment.

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.

Advanced Characterization of Silicone Oil Droplets in Protein Therapeutics Using Artificial Intelligence Analysis of Imaging Flow Cytometry Data.

Monitoring protein particles is increasingly emphasized in the development of biopharmaceuticals due to potential immunogenicity. Accurate quantitation of protein particles is complicated by silicone oil droplets, a common pharmaceutical component in pre-filled syringes. Though silicone oil is typically regarded as harmless, numerous reports have indicated protein adsorption may render these particles with immunostimulatory properties. Imaging flow cytometry (IFC) is an emerging pharmaceutical method capable of capturing high-resolution brightfield and fluorescence imagery from samples in suspension. In this study, we created a data analysis strategy using artificial intelligence (AI) software to classify brightfield images collected with IFC as protein or silicone oil. The AI software performs image classification using deep learning with a convolutional neural network architecture, for identification of subtle morphological phenotypes. The AI model yielded robust classification of particles >2 µm across various sources of protein and silicone oil particles and over the instrument life cycle. Next, the AI model was combined with IFC fluorescence images to differentiate potentially immunogenic protein-adsorbed silicone and innocuous native silicone. The methods reported herein provide added analytical capability for characterization of particulate matter in therapeutic formulations, and may be applied for optimization of protein formulations and evaluation of product consistency.

Cellular image analysis and imaging by flow cytometry.

Imaging flow cytometry combines the statistical power and fluorescence sensitivity of standard flow cytometry with the spatial resolution and quantitative morphology of digital microscopy. The technique is a good fit for clinical applications by providing a convenient means for imaging and analyzing cells directly in bodily fluids. Examples are provided of the discrimination of cancerous from normal mammary epithelial cells and the high-throughput quantitation of fluorescence in situ hybridization (FISH) probes in human peripheral blood mononuclear cells. The FISH application will be enhanced further by the integration of extended depth-of-field imaging technology with the current optical system.

Extended depth of field imaging for high speed cell analysis.

BACKGROUND: Fluoresence microscopy is an extremely useful tool to analyze the intensity, location and movement of fluorescently tagged molecules on, within or between cells. However, the technique suffers from slow image acquisition rates and limited depth of field. Confocal microscopy addresses the depth of field issue via "optical sectioning and reconstruction", but only by further reducing the image acquisition rate to repeatedly scan the cell at multiple focal planes. In this paper we describe a technique to perform high speed, extended depth of field (EDF) imaging using a modified ImageStream system whereby high resolution, multimode imagery from thousands of cells is collected in less than a minute with focus maintained over a 16 microm focal range. METHODS: A prototype EDF ImageStream system incorporating a Wavefront Coded element was used to capture imagery from fluorescently labeled beads. Bead imagery was quantitatively analyzed using photometric and morphological features to assess consistency of feature values with respect to focus position. Jurkat cells probed for chromosome Y using a fluorescence in situ hybridization in suspension protocol (FISHIS) were used to compare standard and Wavefront Coded-based EDF imaging approaches for automated chromosome enumeration. RESULTS: Qualitative visual inspection of bead imagery reveals that the prototype ImageStream system with EDF maintains focus quality over a 16 microm focus range. Quantitative analysis shows the extended depth field collection mode has approximately ten-fold less variation in focus-sensitive feature values when compared with standard imaging. Automated chromosome enumeration from imagery of Jurkat cells probed using the FISHIS protocol is significantly more accurate using EDF imaging. CONCLUSIONS: The use of EDF techniques may significantly enhance the quantitation of cell imagery, particularly in applications such as FISH, where small discrete signals must be detected over a wide focal range within the cell.