Christian E. Zimmerli

This paper introduces qExM, a method to estimate endogenous protein abundance in expanded samples, achieving a 9.4% mean percent error in nuclear pore complex subunit stoichiometry.

This study shows that nuclear pore complexes dilate during cell differentiation and that Nup133 deficiency prevents this dilation, leading to nuclear envelope instability.

DeePiCt is a deep-learning framework for segmenting and localizing macromolecular complexes in cryo-electron tomograms, showing effectiveness in identifying low-abundance structures across species.

The paper uses AI to create a dynamic model of the human nuclear pore complex, showing how the protein scaffold interacts with the nuclear envelope.

This study benchmarks tomographic acquisition schemes for cryo electron tomography to optimize resolution for subtomogram averaging using HIV-1 lattice.