More than 10 million people around the world have active tuberculosis (ATB), and every year more than a million die from it. The majority of patients live in low-resource countries where diagnosing ATB is especially challenging, as newer tests require expensive lab equipment that is frequently not available, and the historic standard of culturing a sputum sample is slow and often not sensitive enough to correctly identify ATB, meaning that many patients whose disease needs treatment are not identified until it is too late.

One of the greatest challenges in treating tuberculosis—the top infectious killer worldwide, according to the World Health Organization (WHO)—is the bacterium's ability to shapeshift rapidly and become resistant to multiple drugs. Identifying resistant strains quickly and choosing the right antibiotics to treat them remains difficult for several reasons, including the bacterium's propensity to grow slowly in the lab, which can delay drug-sensitivity test results by as much as six weeks after initial diagnosis.

Harvard researchers have created a greatly simplified platform for discovering antibiotics that may help solve the rising crisis of resistance to such helpful drugs.