Our cells are regularly bombarded with bouts of DNA damage. Typical rates for double strand breakage, for example, are ten instances per day. While our excellent DNA repair machinery usually maintains the fidelity of our genetic code, this system is not infallible. A number of health problems, including cancer, immunological disorders, and premature aging, have been attributed to mutations and sustained damage. The propensity for unhealthy DNA is largely influenced by genetics, environment, and lifestyle; however, the ways in which these factors affect levels of DNA damage remains an active area of study.

In order to better understand what contributes to the health of our DNA, samples from a huge number of specimens need to be recovered and assessed. Until recently, damage was measured by looking at cells under a fluorescent microscope and manually counting DNA breaks. This approach is not only cumbersome and error prone, but ill-equipped for the purposes of large-scale sampling. However, Berkeley Lab scientist Dr. Sylvain Costes has found a way around this problem. He was able to write an algorithm to automate this process by having a machine scan samples and objectively count DNA breaks. Costes technique was so successful that he decided to launch a biotech startup in 2012, along with colleague Dr. Jonathon Tang, to make this technology available to the public.