{"id":644,"date":"2025-11-06T16:50:41","date_gmt":"2025-11-06T15:50:41","guid":{"rendered":""},"modified":"2025-11-06T16:50:41","modified_gmt":"2025-11-06T15:50:41","slug":"thesis-defense-laetitia-bettarel","status":"publish","type":"buni_events","link":"https:\/\/www.iins.u-bordeaux.fr\/en\/events\/thesis-defense-laetitia-bettarel\/","title":{"rendered":"Thesis defense – <\/span>Laetitia Bettarel"},"content":{"rendered":"

Venue : Centre Broca<\/strong><\/p>\n

\n

Laetitia Bettarel<\/p>\n

Team<\/p>\n

\n

Quantitative imaging of the cell<\/a>
\nIINS<\/p>\n

Title
\n<\/em><\/h3>\n

In-depth Single Molecule Localization Microscopy using adaptive optics and PSF engineering<\/p>\n

Abstract<\/h3>\n

Assessing protein organization and dynamics in their native cellular context provides key insights<\/p>\n

into the molecular mechanisms that govern cell function. Super-resolution microscopy has been a<\/p>\n

major breakthrough in this regard, driving major discoveries in cell, developmental and neuro-<\/p>\n

biology. Amongst these techniques, Single Molecule Localization Microscopy (SMLM) enables<\/p>\n

locating, tracking and counting biomolecules in their cellular environment with nanoscale<\/p>\n

resolution. However, conventional SMLM imaging is restricted by its shallow penetration depth,<\/p>\n

precluding many biological events to be investigated. Performing volumetric SMLM deep within<\/p>\n

complex multicellular samples therefore poses several challenges: achieving efficient optical<\/p>\n

sectioning with high photon collection capabilities, and correcting the optical aberrations<\/p>\n

introduced both by the optical system and the sample, which blur the single molecule signals and<\/p>\n

compromise localization precision and accuracy.<\/p>\n

To address these challenges, we developed in the team a specific light-sheet architecture, named<\/p>\n

soSPIM, which enables in-depth single-molecule imaging and supports the culture and observation<\/p>\n

of complex 3D cellular models. In parallel, Adaptive Optics (AO) has emerged as a powerful<\/p>\n

solution to correct system- and sample-induced aberrations and thereby improve image quality in-<\/p>\n

depth. Recently, we combined soSPIM with AO to achieve volumetric 3D SMLM imaging at the<\/p>\n

whole cell scale. Yet, this implementation still relies on fiducial markers located close to the<\/p>\n

sample, which prevents the effective correction of sample-induced aberrations, that become<\/p>\n

especially significant within multicellular systems. In addition, it uses conventional 3D<\/p>\n

localization approaches, that are non-optimal for fast and accurate in-depth single molecule<\/p>\n

localization.<\/p>\n

In this context, my PhD work focused on developing methodological solutions to extend the<\/p>\n

applicability of the AO-soSPIM imaging platform for in depth SMLM in complex 3D samples.<\/p>\n

First, I developed a fully custom Python-based sensorless AO correction algorithm allowing<\/p>\n

complete control over all parameters of the correction loop, including the integration of user-<\/p>\n

defined image quality metric specifically tailored to the imaging modality and sample type.<\/p>\n

Building on this, I established a systematic framework to assess fiducial-free image-based metrics<\/p>\n

and identify those most sensitive and robust under in-depth SMLM experimental conditions.<\/p>\n

Together, these developments provide a versatile and reliable foundation for restoring diffraction-<\/p>\n

limited performance in photon-limited SMLM acquisitions.<\/p>\n

Second, I investigated deep learning-based single molecule localization frameworks that exploit<\/p>\n

data-driven PSF models to enhance both localization robustness and imaging speed, offering a<\/p>\n

promising alternative to conventional Gaussian fitting in dense or challenging 3D SMLM datasets.<\/p>\n

I also explored experimental PSF modeling strategies to better account for residual aberrations and<\/p>\n

complex PSF deformations in depth. These approaches, which capture PSF shapes beyond the<\/p>\n

Gaussian approximation, aims to improve localization precision and accuracy under aberrated<\/p>\n

conditions.<\/p>\n

Altogether, these methodological developments establish a robust pipeline for aberration-<\/p>\n

corrected, high-resolution 3D SMLM within complex biological 3D samples. By enabling reliable<\/p>\n

volumetric SMLM imaging beyond the coverslip, this work broadens the scope of super-resolution<\/p>\n

imaging toward physiologically relevant 3D models such as spheroids and organoids.<\/p>\n

Jury<\/h3>\n

– R\u00e9mi GALLAND : Directeur de th\u00e8se
\n– Laurent COGNET: Examinateur
\n– Alexandra FRAGOLA: Rapporteuse
\n– Lydia DANGLOT: Rapporteuse
\n– Jean-Baptiste SIBARITA: Invit\u00e9<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

In-depth Single Molecule Localization Microscopy using adaptive optics and PSF engineeringVenue : Centre Broca<\/p>\n","protected":false},"template":"","categories":[],"class_list":["post-644","buni_events","type-buni_events","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.iins.u-bordeaux.fr\/en\/wp-json\/wp\/v2\/buni_events\/644","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iins.u-bordeaux.fr\/en\/wp-json\/wp\/v2\/buni_events"}],"about":[{"href":"https:\/\/www.iins.u-bordeaux.fr\/en\/wp-json\/wp\/v2\/types\/buni_events"}],"wp:attachment":[{"href":"https:\/\/www.iins.u-bordeaux.fr\/en\/wp-json\/wp\/v2\/media?parent=644"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iins.u-bordeaux.fr\/en\/wp-json\/wp\/v2\/categories?post=644"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}