SynopsisImaging 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 FISH probes in human peripheral blood mononuclear cells. The FISH application will be further enhanced by the integration of extended depth of field imaging technology with the current optical system. Keywordsimaging; flow; cytometry; fluorescence; brightfield; darkfield; multispectral Quantifying Cellular Structure in Health and DiseaseThe eukaryotic cell is a highly structured, three-dimensional object containing a wide range of molecular species. The size, shape, and structure of the cell, as well as the abundance, location, and co-location of any of these constituent biomolecules may be of significance in any given clinical situation or research application. For instance, in hematopoiesis, as cells differentiate and mature, different subsets of molecules are expressed that reflect a specialized functional capacity for that unique cell type (e.g., granulocytes vs. lymphocytes). In general the characterization of this array of constituent molecules by imaging or flow cytometry provides insight into the physiological function of any particular cell or alternatively, pathological changes that may have occurred or accrued. In clinical practice and in research settings, cellular evaluation by imaging technologies and flow cytometry provides significant information reflecting the particular cellular phenotype, both normal and pathological. Microscopy provides a wealth of information, but data acquisition rates are slow and analysis is generally subjective. In flow cytometry, data acquisition is rapid and better suited for the evaluation of pathologies present in low frequency, but the data are only intensity-based, thus lacking the morphology that truly lends credence to the analysis.In addition, the assessment and evaluation of cell samples by imaging and flow cytometric techniques is complicated by a number of factors. For instance, changes in a cell type or aCorresponding author for proofs and reprints:
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 ImageStreamÒ system whereby high resolution, multimode imagery from thousands of cells is collected in less than a minute with focus maintained over a 16 lm focal range. Methods: A prototype EDF ImageStream system incorporating a Wavefront Coded TM 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 TM ) were
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.
This work explains the comparison of various dc-dc converters for photovoltaic systems. In recent day insufficient energy and continues increasing in fuel cost, exploration on renewable energy system becomes more essential. For high and medium power applications, high input source from renewable systems like photovoltaic and wind energy system turn into difficult one, which leads to increase of cost for installation process. So the generated voltage from PV system is boosted with help various boost converter depends on the applications. Here the various converters are like boost converter, buck converter, buck-boost converter, cuk converter, sepic converter and zeta converter are analysed for photovoltaic system, which are verified using matlab / simulink.
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 mm 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.
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. '
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