Nuclear-labeling index analysis (NLIA), a software package used to perform accurate automation of cell nuclear-labeling index analysis on immunohistochemically stained rat liver samples.
The nuclear labeling index (labeled nuclei/100 nuclei) and the apoptotic index (apoptotic cells/100 cells) are important parameters of cell growth and death. Automatic counting of labeled nuclei is desirable since manual counting is tedious, time-consuming, and with a greater potential for inaccuracies. A nuclear-labeling index analysis (NLIA) software package was developed in this laboratory to perform the counting process automatically and accurately. This software package consists of an application program NLIA and a set of macros for obtaining nuclear data that is used in Scion Image. It is designed to work cooperatively with Scion Image, Adobe Photoshop, and Microsoft Office. NLIA has two basic functions: building nuclear data files and analyzing nuclear data. A color image captured from an immunohistochemically stained or autoradiographic sample is loaded into NLIA. Nuclear data can be entered into the program manually, automatically, or in combination. In the manual data entering mode, NLIA acts as an object-counting tool, while in the automatic mode it acts as a data picker: picking up the data generated by Scion Image into memory. A method to enter nuclear data (both labeled nuclei and unlabeled nuclei) in the automatic mode is described. The color image is processed in Adobe Photoshop, where the interested color ranges are selected and separated. These are then analyzed in Scion Image with the help of the macros for obtaining nuclear data. Since the advanced particle analysis function is used, the counting process is automatic and rapid. Data from thousands of nuclei can be obtained within seconds. To ensure the accuracy of the analysis, a nuclear data checking and edit feature is employed in NLIA: results of computer-generated counting can be compared with the original color image by overlaying the plot of counting results onto the original color image. In this way any computer counting mistakes can be easily discovered and corrected by the operator. Corrected nuclear data (including nuclear size, location, shape) are then stored in data files. These data files can be used in NLIA to obtain cell density and nuclear labeling indices. Because criteria for obtaining nuclear data (truncation diameter, shape factor) can be set by the operator in NLIA, nuclear size distribution and shape variation can be analyzed. This method provides a fast and accurate way to determine cell nuclear-labeling indices. Currently, Scion Image is a freeware on the internet, and NLIA software package is available from our lab home page. Methods presented here expand the Scion Image ability to analyze color images by using color separation techniques in a commercial graphic application. The instrumentation required can be relatively inexpensive, and the methods described may be useful in studies of cell kinetics, lesion growth, and tumor therapy.
Xu
Y H
YH
Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI 53706, USA. xu@oncology.wisc.edu
Sattler
G L
GL
Edwards
H
H
Pitot
H C
HC
D000818:Animals; D001345:Autoradiography; D002452:Cell Count / Q000379:methods*; D002467:Cell Nucleus / Q000648:ultrastructure*; D003116:Color; D004396:Coloring Agents; D004867:Equipment Design; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D008099:Liver / Q000166:cytology*; D008838:Microcomputers; D008940:Mitotic Index; D051381:Rats; D015203:Reproducibility of Results; D012984:Software
D004396:Coloring Agents