Automated STED microscopy for cell-biological high-throughput assays

Bierbaum, Sebastian

dc.contributor.advisor	Jakobs, Stefan Prof. Dr.
dc.contributor.author	Bierbaum, Sebastian
dc.date.accessioned	2022-11-28T15:22:53Z
dc.date.available	2022-12-05T00:50:09Z
dc.date.issued	2022-11-28
dc.identifier.uri	http://resolver.sub.uni-goettingen.de/purl?ediss-11858/14369
dc.identifier.uri	http://dx.doi.org/10.53846/goediss-9583
dc.format.extent	110 Seiten	de
dc.language.iso	eng	de
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject.ddc	570	de
dc.title	Automated STED microscopy for cell-biological high-throughput assays	de
dc.type	doctoralThesis	de
dc.contributor.referee	Jakobs, Stefan Prof. Dr.
dc.date.examination	2022-10-17	de
dc.description.abstracteng	In the past decade, super-resolution fluorescence microscopy has revolutionized biological research by enabling the study of biological functions down to the molecular scale. Despite profound technological developments, the image acquisition process is, however, still a mainly manual task. Robust automated microscopy techniques and streamlined acquisition schemes will be key to increase the throughput of super-resolution methods to meet the requirements for industrial applications, e.g., in pharmaceutical drug screenings. In this thesis, I established an automated stimulated emission depletion (STED) nanoscopy platform with special attention to the attainable imaging speeds. By implementing camera-based widefield imaging for both cell and feature detection, the throughput of the imaging platform could be considerably increased compared to an approach based solely on laser scanning. A use case from current pharmaceutical research addressing centrosomal clusters in cancer cells was successfully optimized for STED microscopy. Mitotic cells of interest and the centrosomes they contain were identified and localized by means of machine learning and classical image analyses. Extracted widefield feature coordinates were then reproducibly transformed into the confocal image coordinate system for subsequent STED image acquisitions. The feasibility of the proposed acquisition scheme was demonstrated in first automated proof of concept measurements with ten potentially active ingredients. STED images of individual centrosomes revealed the centriole orientation and protein localization with superior accuracy. Automated STED microscopy thus offers the possibility to observe drug-mediated responses with unprecedented detail and can be a valuable additional decision tool for refined mode of action elucidation and target validation in future drug screening campaigns.	de
dc.contributor.coReferee	Janshoff, Andreas Prof. Dr.
dc.contributor.thirdReferee	Hell, Stefan Prof. Dr.
dc.contributor.thirdReferee	Köster, Sarah Prof. Dr.
dc.contributor.thirdReferee	Egner, Alexander Apl. Prof. Dr.
dc.contributor.thirdReferee	Berg, Helmut Dr.
dc.subject.eng	automated microscopy	de
dc.subject.eng	super-resolution fluorescence microscopy	de
dc.subject.eng	STED nanoscopy	de
dc.subject.eng	cell-based assay	de
dc.subject.eng	drug screening	de
dc.subject.eng	centrosomal clustering	de
dc.subject.eng	machine learning	de
dc.subject.eng	deep learning	de
dc.subject.eng	image analysis	de
dc.identifier.urn	urn:nbn:de:gbv:7-ediss-14369-0
dc.affiliation.institute	Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB)	de
dc.subject.gokfull	Biologie (PPN619462639)	de
dc.description.embargoed	2022-12-05	de
dc.identifier.ppn	1826746110
dc.notes.confirmationsent	Confirmation sent 2022-11-28T15:45:02	de

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Das Dokument erscheint in:

GGNB - Göttinger Graduiertenzentrum für Neurowissenschaften, Biophysik und molekulare Biowissenschaften [1114]
GGNB - Göttingen Graduate Center for Neurosciences, Biophysics and Molecular Biosciences

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