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

Diagnostic value of HCMV pp65 antigen detection by FCA for symptomatic and asymptomatic infection: compared to quantification of HCMV DNA and detection of IgM antibody in infants

Human cytomegalovirus (HCMV) can cause symptomatic or asymptomatic infection in infants. One hundred and twenty-six infants were assessed clinically for disease in infantile period. Eighty of them were classified as symptomatic infection on the basis of physical, instrumental, and laboratory findings, 5 were demonstrated by following up to have later developed HCMV disease, and the other 41 infants were classified as asymptomatic infection. HCMV DNA was positive in all urine samples of the symptomatic infants detected by quantitative polymerase chain reaction. HCMV-IgM antibody detected by chemiluminescent immunoassay (CLIA) was positive in 62 of the 85 symptomatic infants, but was negative in all of the samples of asymptomatic infants. HCMV pp65 antigen detected by flow cytometry assay (FCA) was positive in 77 of the 85 symptomatic infants and in none of the asymptomatic infants. The coincidence to symptom of HCMV pp65 antigen detection was higher than those of HCMV DNA and HCMV-IgM antibody detection. The sensitivity, specificity, positive prognostic value and the negative prognostic value of HCMV pp65 antigen detection for diagnosis of HCMV infection was 90.6, 100, 100 and 83.7%, respectively. We concluded that detection of pp65 antigen by FCA is more sensitive for diagnosis of HCMV infection than detection of HCMV-IgM antibody and is better than HCMV DNA quantification for distinguishing the symptomatic and asymptomatic HCMV infection in infants.

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

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.

RESULTS

The DT was built from the measurement data of 2,047 true- and 2,028 false-positive detections, collected from 32 patient samples. By designating misclassifications of false-negatives three times more costly, the 10-fold cross-validation sensitivity, specificity, and misclassification error of the assay was 94.3%, 56.2%, and 25%, respectively. The method was also validated using an independent test set of 21 patient samples, in which similar results were obtained.

CONCLUSIONS

To our knowledge, this study represents the first attempt to improve the accuracy of rare event image cytometry through the implementation of artificial intelligence methodology. Results suggest that cost-sensitive decision tree analysis of digitally measured cellular features vastly improves the performance of rare event image cytometry.

The use of flow cytometry for the detection of CMV-specific antigen (pp65) in leukocytes of kidney recipients

Flow cytometric assay (FCA) was used to detect a cytomegalovirus (CMV) specific antigen (pp65) in CMV-infected fibroblast cells and in leukocytes of kidney recipients. FCA distinguished clearly between the infected and non-infected fibroblast cells. Regarding transplant patients, the FCA was positive when the number of antigenemia assay (AA) positive cells was five or more per 5 x 10(4). Moreover, the percentage of antigenemia-positive cells by FCA correlated well with symptomatic CMV infections.

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood.

Development of a method for direct quantification of cytomegalovirus antigenemia by flow cytometry

Cytomegalovirus (CMV) antigenemia was directly detected in polymorphonuclear leukocytes (PMNLs) from transplant recipients by using flow cytometry (FC). Two fixation and permeabilization methods and seven anti-CMV monoclonal antibodies (MAbs) were evaluated. 1C3, SL20, and NEA-9221 MAbs were more efficacious. The antigenemia detection threshold of FC was 0.05% positive PMNLs, and percentages correlated well with DNA viral load and the appearance of clinical symptoms.

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

One of the major drawbacks in cytomegalovirus (CMV)-antigenaemia detection for diagnosis of active CMV infection is the low number of CMV-antigen positive cells present in peripheral blood. It is therefore necessary to screen large numbers of peripheral blood granulocytes to find only a few antigen-positive cells. We have optimized this detection by testing several monoclonal antibodies (mAb) to CMV-antigens (mAbs C10/C11, C12, BM222, E13 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 C12. 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.

Detection and isolation of rare cells

Cells of biomedical interest are often present in very small numbers (i.e. they are rare), despite their functional significance. The analysis and isolation of previously inaccessible rare cells, such as peripheral hematopoietic stem cells, fetal cells in maternal blood, residual tumor cells or antigen-specific lymphocytes, has now become feasible through the development of new methods. Today, these techniques allow the detection, isolation and analysis of cells less frequent than one in a million. Not many problems in immunology would require higher resolution.

Detection of immunocytochemically stained rare events using image analysis

The detection of rare-event cells circulating in peripheral blood using automated image analysis was evaluated using a model system consisting of cells from a breast cancer cell line (SKBR3) seeded in a mononuclear cell suspension. Slides of cells with optimal morphology were prepared according to an optimized preparation procedure based on centrifugal cytology in combination with formalin fixation. SKBR3 cells were immunocytochemically stained for cytokeratin using the cam 5.2 monoclonal antibody and labelled with alkaline phosphatase using CAS-red as substrate. Because, for optimal segmentation of cell images, plain differences in absorption wavelength are required, the red immunostaining was combined with a green nuclear counter-staining based on ethyl green. Slides were automatically screened for cytokeratin-positive SKBR3 cells resulting in a lowest detectable frequency of one positive cell per 1.87 x 10(6) negative cells. A comparison between manual screening and automated screening for cytokeratin-positive cells showed a high level of correlation (0.9998). For the definition of the total number of objects per slide, two counting procedures were evaluated. Results were close to the visual score with a coefficient of variation of 0.47% for the counting procedure used in this study. It is concluded that optimization of preparation and staining procedures for the detection of rare-event cells using automated image analysis results in optimal image contrast and, consequently, in an increase in sensitivity for detecting rare events.