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Abstract

Biology at the molecular level is driven by macromolecules interacting with each other to form multi-component complexes. To understand the function of macromolecular complexes, quantitative information about their composition and dynamics are essential. Single-molecule fluorescence microscopy offers unique possibilities to provide such information, but relies on the labeling of target molecules with fluorescent markers. In addition, the fluorescence signal emitted by a sample does not readily inform about absolute fluorophore numbers. To measure complex stoichiometries with fluorescence microscopy, it is therefore required to know the fraction of successfully labeled target molecules and to apply a method which infers the number of fluorescent markers from the recorded fluorescence signal. To approach these challenges, I have established and optimized tools to allow for a reliable determination of labeling efficiencies and secondly to successfully determine fluorophore numbers in fluorescently labeled complexes inside cells. In the first part of this study, I show that the labeling efficiency achieved with self-labeling protein tags in living and fixed cells can be calibrated by single-molecule colocalization analysis. An improved and validated data processing pipeline enabled me to systematically study the performance of the self-labeling protein tags, SNAPt-tag and HaloTag across different labeling conditions and with different fluorescent ligands. I found that labeling efficiencies for both tags depend on the ligand used and are limited to sub-stoichiometric levels for all tested ligands and labeling conditions. The highest labeling efficiency for either tag was achieved by labeling of SNAPt-tag with BG-SiR in fixed cells, where a maximum labeling efficiency of 65% was reached. The developed calibration approach provides a generalizable platform for the development and benchmarking of new labeling schemes for quantitative fluorescence microscopy. To address the need for a method to translate fluorescence intensity into absolute fluorophore numbers, quickPBSA was established as a new framework for fluorophore counting by photobleaching step analysis. quickPBSA was validated with simulations and in vitro measurements on DNA origami demonstrating an accessible counting range of up to 35 fluorophores and a 100-fold improvement in computational cost compared to previous algorithms. I could show that by combining improved data acquisition conditions with the quickPBSA framework for photobleaching step detection enables measuring stoichiometries on complexes labeled with up to 32 fluorophores inside cells. In combination, the developments presented in this study provide a comprehensive approach for measuring stoichiometries of protein complexes in situ and put applications in cell biology into perspective.