bioRxiv Science⌕ Search

Biology subjects

Michaud-Gagnon, A.

Publications and source records attributed to Michaud-Gagnon, A..

2 recordsLinked to original sources

Enhancing STED Microscopy via Fluorescence Lifetime Unmixing and Filtering in Two-Species SPLIT-STED

Simultaneous super-resolution imaging of multiple fluorophores remains a major challenge in STimulated Emission Depletion (STED) microscopy due to spectral overlap of STED-compatible fluorophores. The combination of STED microscopy and Fluorescence Lifetime Imaging Microscopy (FLIM) offers a powerful alternative for super-resolved, multiplexed imaging of biological samples but is hindered by lifetime convergence at high depletion powers. Here, we present an analysis method, two-species Separation of Photons by LIfetime Tuning (SPLIT)-STED, that uses a linear system of equations in phasor-based STED-FLIM to enhance both fluorophore unmixing and spatial resolution. It defines the fluorescence signal as a mixture of three lifetime components: the two target fluorophores and a short-lifetime contribution from undepleted peripheral fluorescence photons. Two-species SPLIT-STED disentangles overlapping lifetimes and selectively filters low-resolution signal. The method enables accurate unmixing of spectrally overlapping fluorophores and, by enhancing resolution through lifetime-based filtering, allows the use of lower depletion powers, thereby improving fluorescence lifetime separation.

bioinformatics↗

Development of AI-assisted microscopy frameworks through realistic simulation in pySTED

The integration of artificial intelligence (AI) into microscopy systems significantly enhances performance, optimizing both the image acquisition and analysis phases. Development of AI-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

bioinformatics↗