Biosciences News

A new digital brochure released by Berkeley Lab showcases a suite of biological research software developed in large part by Biosciences Area scientists. In collaboration with the Lab’s Computational Research Division, the National Energy Research Scientific Computing Center (NERSC), and other DOE national labs and academic institutions, scientists created 16 open- and closed-source software and tools in their part to accelerate progress across biological research. Ranging from imaging to omics research to synthetic biology tools, there’s something for everyone.

A collaborative team has developed an atlas that maps gene expression patterns in the Arabidopsis root, profiling nearly 100,000 single root cells and combining the information with previously published datasets. The work was recently published in the journal Developmental Cell and provides a community resource that could help researchers track cell development and how they determine identity, as well as the roles played by neighboring cells in these processes. Learn more here on the JGI website.

Scientists at Berkeley Lab are working to expand our understanding of breast cancer diagnosis and treatment. Recently, a group in the BSE Division developed a framework that enables the transfer of discoveries derived from mouse models to humans. This success will allow breast cancer researchers to better predict how likely a tumor in humans is to metastasize based on how the corresponding cells in mice behaved.

Amy Herr, faculty engineer in the Biological and Systems Engineering Division, has been appointed as Chief Technology Officer (CTO) of the newly established Chan Zuckerberg Biohub Network. As CTO, Herr will help lead the Network’s efforts to advance technologies to observe, measure, and analyze human biology in action.

A team of researchers led by NCXT Director Carolyn Larabell, in collaboration with scientists at Heidelberg University in Germany, used a technique called soft X-ray tomography (SXT) to quickly scan and analyze human lung cells infected with SARS-CoV-2. SXT not only significantly shortens the time frame, but provides more detail—increasing the chances of distinguishing subtle changes in the cell.