Alzheimer’s Disease

Chung, C.W., Stephens, A.D., Konno, T., Ward, E., Avezov, E., Kaminski, C.F., Hassanali, A.A. and Kaminski Schierle, G.S., 2022. Intracellular Aβ42 aggregation leads to cellular thermogenesis. Journal of the American Chemical Society, 144(22), pp.10034-10041.

Collins, S., van Vliet, L., Gielen, F., Janeček, M., Valladolid, S.W., Poudel, C., Fusco, G., De Simone, A., Michel, C., Kaminski, C.F. and Spring, D.R., 2022. A unified in vitro to in vivo fluorescence lifetime screening platform yields amyloid β aggregation inhibitors. bioRxiv, pp.2022-03.

Lu, M., Williamson, N., Mishra, A., Michel, C.H., Kaminski, C.F., Tunnacliffe, A. and Schierle, G.S.K., 2019. Structural progression of amyloid-β Arctic mutant aggregation in cells revealed by multiparametric imaging. Journal of Biological Chemistry, 294(5), pp.1478-1487.

Young, L.J., Schierle, G.S.K. and Kaminski, C.F., 2017. Imaging Aβ (1–42) fibril elongation reveals strongly polarised growth and growth incompetent states. Physical Chemistry Chemical Physics, 19(41), pp.27987-27996.

Machine Learning for FLIM Optimisation

Kapsiani, S., Läubli, N.F., Ward, E.N., Fernandez-Villegas, A., Mazumder, B., Kaminski, C.F. and Kaminski Schierle, G.S., 2025. Deep learning for fluorescence lifetime predictions enables high-throughput in vivo imaging. bioRxiv, pp.2025-02.

Spike Sorting for AD Research

Brockhoff, M., Träuble, J., Middya, S., Fuchsberger, T., Fernandez-Villegas, A., Stephens, A., Robbins, M., Dai, W., Haider, B., Vora, S. and Läubli, N.F., 2024. Machine learning-based spike sorting reveals how subneuronal concentrations of monomeric Tau cause a loss in excitatory postsynaptic currents in hippocampal neurons. bioRxiv, pp.2024-02.