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Cytometry. 2000 Apr 1;39(4):285-94.

Ultra-rare-event detection performance of a custom scanning cytometer on a model preparation of fetal nRBCs.

Author information

1
Department of Bioengineering and Institute for Biomedical Engineering, University of California at San Diego, La Jolla, California 92093-0412, USA.

Abstract

BACKGROUND:

The performance of a fully automated scanning cytometer incorporating previously reported high-precision autofocus and accurate image segmentation was evaluated for the detection of "ultra-rare" cells using a model of fetal nucleated red blood cells (fnRBCs) in the maternal circulation. These distinctive scanning cytometry techniques were expected to markedly improve sensitivity and specificity.

METHODS:

Normal adult blood and fetal red blood cells were stained with fluorescein isothiocyanate-conjugated anti-fetal hemoglobin and 4,6-diamidino-2-phenylindole, a nuclear dye. Adult cells were spiked with fetal cells to create ratios of about 1 fnRBC in 10(7) nucleated cells and deposited in monolayers on slides using centrifugal cytology. Rare-event performance parameters were reviewed, formalized, and applied to test the new instrument using this cell model.

RESULTS:

Fifteen slides were analyzed to establish performance by comparison with manual detection, and four sets of four slides each were then scanned to explore the limit of detection. Results were an average sensitivity of 91%, an average specificity error of 12.3 false-positives per million cells, and repeatability of 100% at a cell analysis rate of 862 Hz. With addition of a quick interactive step subsequent to scanning, the false-positive rate dropped to a total of only one artifact over the 15 experiments. The instrument succeeded at locating 1 fnRBC in 20 million adult cells, the lowest limit of detection tested.

CONCLUSION:

This consistently high performance, coupled with the capability of scanning arbitrarily large numbers of cells, validates the considerable potential of precise high-speed autofocus and accurate real-time image segmentation for ultra-rare event detection.

PMID:
10738281
[Indexed for MEDLINE]

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