Ahmed Sharara

Visualization of proteins and sub-cellular structures such as nuclei, and cell organelles using fluorescent dyes or fluorescently tagged antibodies (immunostaining) has been a cornerstone of biological research. However, the current quantification method involves time-consuming and labor-intensive manual counting using ImageJ. Further, these issues tend to introduce unintentional biases, inconsistencies, and are not feasible for large datasets. While published ImageJ macros address the efficiency issue, they often fail to accurately quantify images with varying cell density. Here, we introduce AutoCount, an open-source ImageJ macro, designed to accelerate the quantification of fluorescently labeled images. AutoCount integrates fluorescence intensity maxima with area thresholding, ensuring precise segmentation and quantification across diverse experimental conditions. This macro requires only 3 user inputs relative to other similar scripts that require >10 user inputs. Consequently, AutoCount significantly improves efficiency, reducing the analysis time of 100 images by ~10-fold for high confluency images (>80%) and ~3- and ~6-fold for low (<50%), and medium (50-70%) confluence respectively. Notably, its accuracy was not significantly different relative to manual counts. We showcased AutoCount’s accurate quantification of cell viability (Live/Dead) and pluripotency efficiency (OCT4+ and NANOG+)—two of the multiple applications of a cell-counting program. Importantly, all these validations showed no significant differences relative to manual counts. By bridging the gap between manual accuracy and the efficiency demanded by modern research workflows, AutoCount provides a robust, versatile, and user-friendly solution for fluorescence image analysis.