Ms. Karen Caicedo Profile

Karen Caicedo

at Lab Photonique Numérique et Nanosciences

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Proceedings Article | 30 May 2022 Presentation

Deep tissue 3D single-particle tracking using self-interference in near-infrared

Karen Caicedo, Antony Lee, Pierre Bon, Laurent Cognet

Proceedings Volume PC12144, PC1214407 (2022) https://doi.org/10.1117/12.2621018

KEYWORDS: Tissues, Near infrared, Quantum dots, Wavefronts, Video, Tissue optics, Super resolution microscopy, Super resolution, Microscopy, Luminescence

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Fluorescence microscopy has succeeded in attaining super-resolution localization of single emitters in cellular biology. However, 3D localization deep inside tissue is still challenging. A few years ago, we developed SELFI: self-interference 3D super-resolution microscopy, a framework for 3D single-molecule localization within multicellular specimens and tissues. Here, we extend the capability of SELFI to the near-infrared (NIR) region where carbon nanotubes (CNTs) are strong emitters. The aim of this work is to develop NIR SELFI for single-particle tracking applications of CNTs in live brain tissues or NIR quantum dots. SELFI uses a diffraction grating placed on the optical path of the sample image, generating an interference pattern within diffraction limited images of point emitters. A single image obtained with NIR SELFI contains two independent variables: the intensity distribution to extract the intensity centroid to determine the lateral localization, and the wavefront curvature (provided by the interfringes) to get the axial super-localization. SELFI was first developed to localize red emitting dyes and quantum dots. The performance of the system is examined by means of the standard deviation and root mean square error of the localizations. The experiments performed show that the 3D-precision and accuracy achieved with NIR SELFI are both below 100 nm for emission around 1000 nm and high photon budget. Therefore, we can now achieve 3D localization in the NIR, permitting 3D single-particle tracking of CNTs at video rate in complex environments.

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