Current imaging technology allows scientists to view tissue samples at such high resolution that they can gather information about individual cells. Looking at a high-resolution image of a tumor, for example, an oncologist can locate and measure the amount of a specific mutant protein in a cancer cell. The information gleaned from image-based single-cell analysis can aid both in diagnostics and tracking disease progression.

But it is one thing to generate such high-resolution images, and quite another to analyze them: a single slide containing five square centimeters of tissue, or about ten million cells, can yield up to a terabyte of data. (For reference, that’s about eight times as much as the average smart phone’s capacity.) Until recently, scientists were hard-pressed to find software up to the challenge.

Now they are in luck. This month, former Damon Runyon Quantitative Biology Fellow Denis Schapiro, PhD, and his team at Heidelberg University unveiled an open-source, freely available software program called MCMICRO that can transform whole-slide images into single-cell data. Given a set of images from a microscope, MCMICRO automatically corrects for non-uniform lighting, stitches together images from different fields of view, and segments the images into single cells. Then, researchers can direct the program to perform various types of single-cell analysis, identifying a cell’s shape, location, and molecular characteristics.

“A lot of people wonder whether they are analyzing their multi-terabyte images correctly,” says co-creator Clarence Yapp in a video tutorial for the software. In the background, a scientist flattens his images with an iron and then starts cutting them up with scissors. “If you have such questions, MCMICRO might be what you need.”

MCMICRO is designed to be community-supported, meaning users can add modules to perform different analyses, answer each other’s questions, and share test datasets. Since its release, the software has already been instrumental in building two human tumor “atlases,” or detailed maps of a cancer’s cellular and molecular features used to study its behavior.

Learn more in Nature and explore the software here.