Quantification of Dual Reporter Cell Cultures via Image Analysis

The successful performance of experiments is tightly related to the efficiency and reliability of the analytical methods used for the data analysis. This is particularly true when data is collected as images and the quantification of the biological effect behind the picture is often an issue. Consistent evaluation represents a challenge even for experienced biologists. Even though other techniques allow the quantification of fluorescent cell populations, (FACS, among others) image analysis is widely used. For this reason there is an increasing need of methods for reliable and reproducible quantification of images. To this aim a new algorithm for image analysis has been developed that allows rapid quantification of fluorescent Dual Reporter cells populations. These cells can express RFP (αSMA) or EGFP (collagen), and, via epi-fluorescence, come up as red or green, or yellow when RFP and GFP co-localize within the same cell. The algorithm can quantify and classify these sub-sets, by using a fuzzy, two-pass class partitioning, in addition to Difference of Gaussians and watershed segmentation techniques for blob detection and extraction. Results show average errors in the 2-6% range and an analysis time of 6-9 seconds per acquired image. The algorithm was validated against manual quantification techniques and compared to other state-of-the-art software. It allows rapid detection and extraction of Dual Reporter cells distributions in acquired epi-fluorescence optical fields. Finally, thanks to the implementation of semi-automated calibration procedures, the algorithm constitutes a totally objective instrument for quantification and classification measurements, especially when class boundaries are not sharply defined.