Hi-res microscopes give biologists petabytes of data. Scientists are creating an AI assistant to make sense of it. - Berkeley News

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Hi-res microscopes give biologists petabytes of data. Scientists are creating an AI assistant to make sense of it.

At UC Berkeley, high-resolution microscopes are generating images of cells and embryos day and night, collecting massive amounts of data to train an AI model for living biological systems.

By Robert Sanders

A sequence of high-resolution images showing a cell dividing into three daughter cells, a rare event captured by the MOSAIC microscope in 5D. The images come from the first 3D videos of such an event, which was captured in cancerous pig epithelial cells

Advanced Bioimaging Center/UC Berkeley

May 22, 2026

In a cramped, windowless room on the University of California, Berkeley campus, two bespoke microscopes — each a Swiss Army knife for high-resolution imaging — operate around the clock gathering data that will help train a game-changing technology for the field of biology: AI.

The identical microscopes, described this week in the journal Nature Methods, squeeze a dozen types of high-powered microscopes into a single machine, from standard phase contrast to the latest lattice light-sheet technology — easily switchable with the push of a button. Called MOSAIC (Multimodal Optical Scope with Adaptive Imaging Correction), it has already been recreated in more than a dozen labs worldwide thanks to preprints and elaborate assembly instructions disseminated over the past six years.

At UC Berkeley, it is one in a lineup of improved imaging technologies that could forever alter the field of biology, the researchers say. The microscopes can track over seconds, hours or days the development of live specimens, ranging from molecules and cells to entire embryos, gathering huge amounts of data that will allow biologists to track cells as they move through tissue, the evolution of internal cellular structures and even the shuttling of proteins and other molecules within the cell.

All this data — measured in petabytes, the equivalent of about 500 billion pages of text — requires the analytic ability of a large “vision” language model (LVLM), like ChatGPT. Building an LVLM or AI that can deal with petabytes of imaging data is now one of the main focuses of a team of microscopists, physicists, biologists and computer scientists in Berkeley’s Advanced Bioimaging Center, which hopes to create a first-of-its-kind Cell Observatory.

“Life has to be studied in living tissue, holistically, and over fast timescales and for long periods of time,” said Eric Betzig, a Berkeley professor of molecular and cell biology and of physics who won the 2014 Nobel Prize in Chemistry for the development of super-resolution fluorescence microscopy — a version of which is now incorporated into MOSAIC. “You can’t study something as complex as a cell or organism just by looking at the parts individually — there are something like 40 million protein molecules alone of 20,000 different types. With our microscopes, we can image everything from single molecules to whole organisms at high resolution, following as many players as we can to understand natural physiological interactions in the cell.”

Video of the neurons in a 100 x 100 x 400 micron volume of the brain of a live mouse, obtained with two-photon microscopy and adaptive optics. (Credit: Fu, Liu, Milkie, Ruan et al, Nature Methods)

Betzig, a Howard Hughes Medical Institute investigator, refers to the imaging data as five-dimensional, or 5D: three spatial dimensions, plus time and color. The color comes from fluorescent labels that allow scientists to track multiple subcellular structures simultaneously — organelles, membranes, the cytoskeleton and more — as they migrate, change shape, divide and interact over time.

“We are the world’s best at collecting data at 5D, and have been for a decade,” he said. “But we don’t know how to interpret the data at scale; we can’t think in petabytes and we don’t see in 5D. That’s why we’re developing a 5D AI — it’s a sherpa to guide us.”

MOSAIC’s development was led by Srigokul “Gokul” Upadhyayula, an assistant professor in residence in molecular and cell biology who had earlier worked with Betzig on other high-resolution techniques, the adaptive optical lattice light-sheet microscope, and the expansion lattice light-sheet microscope — both now part of MOSAIC.

“Biology is entering an era in which the data are too complex and too large to interpret by human inspection alone,” he said. “A biologist may understand the biological question deeply, but still lack the computational tools and infrastructure needed to process, analyze and quantify what they are seeing. We need to build a mind that can reason natively with 3D movies of living biological systems and let us query those dynamics through language — akin to a ChatGPT for biology.”

Time-lapse images of...