Identifying and quantifying apoptosis: navigating technical pitfalls.

Apoptosis or programmed cell death is often altered in malignancies and is frequently determined by the terminal transferase-mediated nick end labeling technique (TUNEL). However, commercially available protocols can produce high background and false-positive staining, which renders the distinction between apoptosis and necrosis difficult. In an attempt to develop a rapid and reproducible method for detecting and quantifying apoptosis, we coupled optimization of the Apoptag Plus Peroxidase In Situ Apoptosis Detection kit with quantitative histomorphometric computer imaging software using the Bacus Laboratories Incorporated Slide Scanner (BLISS). Multiple (200-350) unique 40x images were scanned using the BLISS system and downloaded into the WebSlide Browser program, creating a permanent, scanned record of the area assessed. The stored images were counted, with the final analysis simultaneously taking into account cells that were immunohistochemically positive and the histology of the surrounding cells to reduce the possibility of false positive and negative staining. In addition, cells with equivocal staining can be simultaneously reviewed by other technologists with networked WebSlide Browser access to the same images. Our data show that the advantages offered by the BLISS imaging software greatly reduce the potential drawbacks of using the TUNEL method as a sole means of quantification.

Comparison of quantitative histomorphometry and DNA ploidy in tissue sections of prostate carcinoma.

OBJECTIVE: To examine the ability of quantitative histomorphometry to predict DNA ploidy of prostate carcinoma in biopsy tissue sections assigned after quantitation by nuclear digital image analysis. STUDY DESIGN: Thirty-five diploid, 35 tetraploid and 35 aneuploid prostatic carcinomas in biopsies, assessed by the CAS 200 image analyzer (Bacus Laboratories, Lombard, Illinois, U.S.A.), were reevaluated by the Bacus Laboratories Incorporated Slide Scanner, a microscope that quantifies histologic images. Thirty-one histomorphometric features from cancer cells were captured at 40 x magnification, averaged across tilesfor each case and incorporated into a multivariate discriminant model to determine which features predicted ploidy interpretation by nuclear image analysis using the CAS 200. RESULTS: On average, 60 and 15 minutes were required to perform nuclear image analysis and histomorphometry, respectively. The multivariate discriminant model identified configurable run length, difference variance, contrast, inverse difference moment, sum entropy and diagonal variance as histomorphometry features capable of distinguishing diploid from nondiploid tumors (P < .05). Cross-validation studies showed the model correctly classified 74.3% of the diploid and 57.1% of the nondiploid cases. CONCLUSION: Quantitative histomorphometry can predict the ploidy of prostate carcinoma in biopsy tissue sections. Quantitative histomorphometry has potential as a method of rapidly assessing DNA ploidy otherwise earmarked for nuclear image analysis, resulting in savings of time and expense.