Development and application of molecular and computational tools to image copper in cells

Copper is a trace element which is essential for many biological processes. A deficiency or excess of copper(I) ions, which is its main oxidation state of copper in cellular environment, is increasingly linked to the development of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease (PD and AD). The regulatory mechanisms for copper(I) are under active investigation and lysosomes which are best known as cellular “incinerators” have been found to play an important role in the trafficking of copper inside the cell. Therefore, it is important to develop reliable experimental methods to detect, monitor and visualise this metal in cells and to develop tools that allow to improve the data quality of microscopy recordings. This would enable the detailed exploration of cellular processes related to copper trafficking through lysosomes. The research presented in this thesis aimed to develop chemical and computational tools that can help to investigate concentration changes of copper(I) in cells (particularly in lysosomes), and it presents a preliminary case study that uses the here developed microscopy image quality enhancement tools to investigate lysosomal mobility changes upon treatment of cells with different PD or AD drugs. Chapter I first reports the synthesis of a previously reported copper(I) probe (CS3). The photophysical properties of this probe and functionality on different cell lines was tested and it was found that this copper(I) sensor predominantly localized in lipid droplets and that its photostability and quantum yield were insufficient to be applied for long term investigations of cellular copper trafficking. Therefore, based on the insights of this probe a new copper(I) selective fluorescent probe (FLCS1) was designed, synthesized, and characterized which showed superior photophysical properties (photostability, quantum yield) over CS3. The probe showed selectivity for copper(I) over other physiological relevant metals and showed strong colocalization in lysosomes in SH-SY5Y cells. This probe was then used to study and monitor lysosomal copper(I) levels via fluorescence lifetime imaging microscopy (FLIM); to the best of my knowledge this is the first copper(I) probe based on emission lifetime. Chapter II explores different computational deep learning approaches for improving the quality of recorded microscopy images. In total two existing networks were tested (fNET, CARE) and four new networks were implemented, tested, and benchmarked for their capabilities of improving the signal-to-noise ratio, upscaling the image size (GMFN, SRFBN-S, Zooming SlowMo) and interpolating image sequences (DAIN, Zooming SlowMo) in z- and t-dimension of multidimensional simulated and real-world datasets. The best performing networks of each category were then tested in combination by sequentially applying them on a low signal-to-noise ratio, low resolution, and low frame-rate image sequence. This image enhancement workstream for investigating lysosomal mobility was established. Additionally, the new frame interpolation networks were implemented in user-friendly Google Colab notebooks and were made publicly available to the scientific community on the ZeroCostDL4Mic platform. Chapter III provides a preliminary case study where the newly developed fluorescent copper(I) probe in combination with the computational enhancement algorithms was used to investigate the effects of five potential Parkinson’s disease drugs (rapamycin, digoxin, curcumin, trehalose, bafilomycin A1) on the mobility of lysosomes in live cells.Open Acces