Automated image cytometry for detection of rare, viral antigen‐positive cells in peripheral blood

Ploem-Zaaijer, J. J.; Mesker, Wilma E.; Boland, Greet J.; Sloos, Willem C. R.; Rijke, Frans M. van de; Jiwa, Mehdi; Raap, Anton K.

doi:10.1002/cyto.990150304

Cited by 16 publications

(20 citation statements)

References 16 publications

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“…These include both flow cytometry-based techniques and solid-phase image scanning cytometry techniques (3,4,(6)(7)(8). The latter usually uses a planar multielement photodetector, such as a charge-coupled device (CCD) camera, to make images from adjacent elements across the specimen, followed by scene segmentation and use of pattern recognition algorithms to locate the particular targets (9)(10)(11)(12)(13)(14)(15).…”

mentioning

confidence: 99%

Automated detection of rare‐event pathogens through time‐gated luminescence scanning microscopy

Lu¹,

Jin²,

Leif³

et al. 2011

Cytometry Pt A

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Many microorganisms have a very low threshold (\10 cells) to trigger infectious diseases, and, in these cases, it is important to determine the absolute cell count in a lowcost and speedy fashion. Fluorescent microscopy is a routine method; however, one fundamental problem has been associated with the existence in the sample of large numbers of nontarget particles, which are naturally autofluorescent, thereby obscuring the visibility of target organisms. This severely affects both direct visual inspection and the automated microscopy based on computer pattern recognition. We report a novel strategy of time-gated luminescent scanning for accurate counting of rare-event cells, which exploits the large difference in luminescence lifetimes between the lanthanide biolabels, [100 ls, and the autofluorescence backgrounds, \0.1 ls, to render background autofluorescence invisible to the detector. Rather than having to resort to sophisticated imaging analysis, the background-free feature allows a single-element photomultiplier to locate rare-event cells, so that requirements for data storage and analysis are minimized to the level of image confirmation only at the final step. We have evaluated this concept in a prototype instrument using a 2D scanning stage and applied it to rare-event Giardia detection labeled by a europium complex. For a slide area of 225 mm 2 , the time-gated scanning method easily reduced the original 40,000 adjacent elements (0.075 mm 3 0.075 mm) down to a few ''elements of interest'' containing the Giardia cysts. We achieved an averaged signal-to-background ratio of 41.2 (minimum ratio of 12.1). Such high contrasts ensured the accurate mapping of all the potential Giardia cysts free of false positives or negatives. This was confirmed by the automatic retrieving and time-gated luminescence bioimaging of these Giardia cysts. Such automated microscopy based on time-gated scanning can provide novel solutions for quantitative diagnostics in advanced biological, environmental, and medical sciences. ' 2011 International Society for Advancement of Cytometry

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mentioning

confidence: 99%

Automated detection of rare‐event pathogens through time‐gated luminescence scanning microscopy

Lu¹,

Jin²,

Leif³

et al. 2011

Cytometry Pt A

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“…Despite obvious differences in system design, the performance measures of our automated image cytometer are comparable to the LEYTAS system, developed by PloemZaaijer et al (8). The LEYTAS detection method involves the brightfield microcopy-based scanning of immunoenzymatcally labeled leukocytes at high (20ϫ) magnification and the identification of CMV-infected cells by their higher mean optical density.…”

Section: Discussionmentioning

confidence: 99%

“…An automated image cytometer (LEYTAS) that permits the objective classification of immuncytochemically labeled leukocytes has been developed to improve the accuracy and throughput of CMV antigenemia diagnostics (8). The identification of target cells in this and other rare event cytometers is performed by applying a combination of preset ranges of discriminating image analysis parameters, or finding parameters, on digitally acquired microscope images (9 -14).…”

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confidence: 99%

Automated detection of immunofluorescently labeled cytomegalovirus‐infected cells in isolated peripheral blood leukocytes using decision tree analysis

et al. 2004

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Background: Cytomegalovirus (CMV) infection continues to be a major problem for immunocompromised patients. Detection of viral antigens in leukocytes (antigenemia assay) is widely used for the diagnosis of CMV infection and for guiding antiviral therapy. The antigenemia technique, contingent upon the manual microscopic analysis of rare cells, is a laborious task that is subject to human error. In this study, we combine automated microscopy with artificial intelligence for reliable detection of fluorescently labeled CMV-infected cells. Methods: Cytospin preparations of peripheral blood leukocytes were immunofluorescently labeled for the CMV lower matrix phosphoprotein (pp65) and scanned in the Rare Event Imaging System (REIS), a fully automated image cytometer. The REIS detected potential positive objects and digitally recorded 49 measured cellular features for each identified case. The measurement data of these objects were analyzed by the See5 decision tree (DT) algorithm to ascertain whether they were true-positive detections.

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“…Pretransplant determined factors, e.g. type of transplant and CMV serology of the donor and recipient, should also be taken into account when identifying risk groups for active and/or symptomatic CMV infections [1-3, 12,13].…”

Section: Discussionmentioning

confidence: 99%

Detection improvement of cytomegalovirus antigen in human peripheral blood using monoclonal antibodies and automated reading of cell preparations

Boland¹,

Mesker²,

Doorn³

et al. 1995

Eur J Clin Investigation

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Abstract. One of the major drawbacks in cytomegalovirus (CMV)-antigenaemia detection for diagnosis of active CMV infection is the low number of CMVantigen positive cells present in peripheral blood. It is therefore necessary to screen large numbers of peripheral blood granulocytes to find only a few antigenpositive cells. We have optimized this detection by testing several monoclonal antibodies (mAb) to CMV-antigens (mAbs ClOjCl I , C12, BM222, El3 and SL20). In total 550 blood samples from 40 patients were investigated. More blood samples were found positive with mAb C12 than with the other mAbs. Also the average number of positive cells per slide was highest for mAb C 12. Furthermore, duplicate slides were examined automatically using an image analysis system (LEYTAS) and compared to visual detection (cytospin slides). The detection sensitivity of both screening methods was compared for mAb C12. In total 360 slides were analysed, from positive as well as negative blood samples. The sensitivity of the automated screening was 93% and for the visual evaluation of the cytospin slides 73%. In conclusion, mAb C12 was the most suitable of the mAbs tested for detection of antigenaemia, and automatic detection of CMV antigenaemia with image analysis of slides is a sensitive method due to the large numbers of cells that can be screened.

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Automated image cytometry for detection of rare, viral antigen‐positive cells in peripheral blood

Cited by 16 publications

References 16 publications

Automated detection of rare‐event pathogens through time‐gated luminescence scanning microscopy

Automated detection of rare‐event pathogens through time‐gated luminescence scanning microscopy

Automated detection of immunofluorescently labeled cytomegalovirus‐infected cells in isolated peripheral blood leukocytes using decision tree analysis

Detection improvement of cytomegalovirus antigen in human peripheral blood using monoclonal antibodies and automated reading of cell preparations

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