Thesis defense – Laetitia Bettarel

Quantitative imaging of the cell
IINS

Title

In-depth Single Molecule Localization Microscopy using adaptive optics and PSF engineering

Abstract

Assessing protein organization and dynamics in their native cellular context provides key insights

into the molecular mechanisms that govern cell function. Super-resolution microscopy has been a

major breakthrough in this regard, driving major discoveries in cell, developmental and neuro-

biology. Amongst these techniques, Single Molecule Localization Microscopy (SMLM) enables

locating, tracking and counting biomolecules in their cellular environment with nanoscale

resolution. However, conventional SMLM imaging is restricted by its shallow penetration depth,

precluding many biological events to be investigated. Performing volumetric SMLM deep within

complex multicellular samples therefore poses several challenges: achieving efficient optical

sectioning with high photon collection capabilities, and correcting the optical aberrations

introduced both by the optical system and the sample, which blur the single molecule signals and

compromise localization precision and accuracy.

To address these challenges, we developed in the team a specific light-sheet architecture, named

soSPIM, which enables in-depth single-molecule imaging and supports the culture and observation

of complex 3D cellular models. In parallel, Adaptive Optics (AO) has emerged as a powerful

solution to correct system- and sample-induced aberrations and thereby improve image quality in-

depth. Recently, we combined soSPIM with AO to achieve volumetric 3D SMLM imaging at the

whole cell scale. Yet, this implementation still relies on fiducial markers located close to the

sample, which prevents the effective correction of sample-induced aberrations, that become

especially significant within multicellular systems. In addition, it uses conventional 3D

localization approaches, that are non-optimal for fast and accurate in-depth single molecule

localization.

In this context, my PhD work focused on developing methodological solutions to extend the

applicability of the AO-soSPIM imaging platform for in depth SMLM in complex 3D samples.

First, I developed a fully custom Python-based sensorless AO correction algorithm allowing

complete control over all parameters of the correction loop, including the integration of user-

defined image quality metric specifically tailored to the imaging modality and sample type.

Building on this, I established a systematic framework to assess fiducial-free image-based metrics

and identify those most sensitive and robust under in-depth SMLM experimental conditions.

Together, these developments provide a versatile and reliable foundation for restoring diffraction-

limited performance in photon-limited SMLM acquisitions.

Second, I investigated deep learning-based single molecule localization frameworks that exploit

data-driven PSF models to enhance both localization robustness and imaging speed, offering a

promising alternative to conventional Gaussian fitting in dense or challenging 3D SMLM datasets.

I also explored experimental PSF modeling strategies to better account for residual aberrations and

complex PSF deformations in depth. These approaches, which capture PSF shapes beyond the

Gaussian approximation, aims to improve localization precision and accuracy under aberrated

conditions.

Altogether, these methodological developments establish a robust pipeline for aberration-

corrected, high-resolution 3D SMLM within complex biological 3D samples. By enabling reliable

volumetric SMLM imaging beyond the coverslip, this work broadens the scope of super-resolution

imaging toward physiologically relevant 3D models such as spheroids and organoids.

Jury

– Rémi GALLAND : Directeur de thèse
– Laurent COGNET: Examinateur
– Alexandra FRAGOLA: Rapporteuse
– Lydia DANGLOT: Rapporteuse
– Jean-Baptiste SIBARITA: Invité