TY - JOUR
T1 - Depth-enhanced high-throughput microscopy by compact PSF engineering
AU - Opatovski, Nadav
AU - Nehme, Elias
AU - Zoref, Noam
AU - Barzilai, Ilana
AU - Orange Kedem, Reut
AU - Ferdman, Boris
AU - Keselman, Paul
AU - Alalouf, Onit
AU - Shechtman, Yoav
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.
AB - High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.
UR - http://www.scopus.com/inward/record.url?scp=85195533814&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41467-024-48502-y
DO - https://doi.org/10.1038/s41467-024-48502-y
M3 - مقالة
C2 - 38849376
SN - 2041-1723
VL - 15
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 4861
ER -