Are you using immunofluorescence (IF) to examine tumor cells but tired of manually sifting through raw imaging data to get the information you need?

NCI-funded researchers have a new tool called “GammaGateR” that gives you a semi-automated solution for sorting and quantifying protein biomarkers and other important cellular information, all while reducing the time for manual segmentation.

As noted by first author, Jiangmei Xiong, of Vanderbilt University, GammaGateR offers important technical advantages. She said, “You can use GammaGateR to identify marker-positive cells and visualize the results, allowing you to compare key cancer-related features such as immune cell populations.”

The tool operates on a “closed-form Gamma mixture model,” a statistical model that lets you assign probability to specific marker’s expression. According to corresponding author, Dr. Simon Vandekar, also of Vanderbilt, “this novel approach to differentiating cell characteristics in multiplexed IF data is more computationally efficient than traditional Gamma mixture models, saving time and giving you more consistent results.”

He added, “GammaGateR offers you a tailored-to-slide model for estimating marker expression in IF data, even when those markers vary considerably across slides.”

The researchers are refining this algorithm. For example, they’re working now to boost its ability to capture pixel-level information. Said Dr. Vandekar, “GammaGateR is highly accurate when looking at segmented cell-level data. But we think we can improve that accuracy by looking at pixel intensity patterns within and around each cell.”

NCI funding for this study included grants supported by the Human Tumor Atlas Network (HTAN), the Translational and Basic Science Research in Early Lesions (TBEL) Program, and Specialized Programs of Research Excellence (SPOREs) Center.