2023 Rosalind Franklin Medal Prize

Professor Clemens Kaminski has made seminal contributions in the development of advanced optical methods for the exploration of the physics, chemistry and biology of living systems on scales ranging from the single molecule to full organisms. His research spans both linear and nonlinear optical methods and includes theoretical and experimental work. His methods have led to ground-breaking discoveries in the study of protein misfolding diseases, in virus research, and in the study of organelle–organelle interactions in living cells.

Kaminski’s group was the first to apply the concept of optical super-resolution imaging for the study of protein misfolding reactions directly within the cell and he was able to ‘film’, molecule by molecule, the self-assembly of Alzheimer’s disease-causing proteins into amyloid fibrils. He observed directly with such methods that amyloid growth is paused when fibril ends are capped by proteins that fold into a conformation where they do not provide the correct template for subsequent proteins to attach. The finding is key to the understanding of amyloid formation and has medical significance: promoting capped end states, for example with a small molecule drug, may provide for a therapeutic strategy to stop amyloid growth, and thus disease progression.

His group has bridged such in vitro biophysical studies with studies in cells and shown how protein aggregates propagate from cell to cell and act as templates to recruit endogenous proteins, causing them to aggregate. The findings bring molecular-level insights into the pathology of patients suffering early onset dementias upon repeated brain trauma: traumatic stress, for example during contact sports, causes brain cells to die and release amyloids, which subsequently triggers aggregation in adjacent healthy cells.

Kaminski’s current work is focused on the dynamic imaging of the intracellular machinery in the context of such brain diseases. His group has developed high-speed super-resolution methods and machine learning algorithms to study organelle function in living cells. They discovered that the very rapid response of the endoplasmic reticulum (ER) networks to adapt to metabolic and homeostatic change in the cell is driven by another organelle, the lysosome, which actively drags and pulls the ER network to sites of local demand. This discovery provides fundamentally new understanding in the context of ER-mediated diseases of the brain.