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Published June 15, 2021 | Published + Supplemental Material + Submitted
Journal Article Open

Super-resolution label-free volumetric vibrational imaging

Abstract

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 78 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

Additional Information

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 17 December 2020; Accepted 07 May 2021; Published 15 June 2021. We thank Xun Wang and Dr. Lilien Voong for fruitful discussions. We are grateful to Can Li and Prof. Marianne Bronner for sharing the zebrafish embryo slices. We acknowledge Prof. Viviana Gradinaru for sharing resources and the helpful discussions. Chenxi Qian acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC Postdoctoral Fellowship). Lu Wei acknowledges the support of the National Institutes of Health (NIH Director's New Innovator Award, DP2 GM140919-01), Amgen (Amgen Early Innovation Award), and the start-up funds from the California Institute of Technology. Data availability: The authors declare that all data supporting the findings of the present study are available in the article and its supplementary figures and tables, or from the corresponding author upon request. Code availability: MATLAB code used for PSF determination and Python code for U-Net training and prediction in this paper is available at https://github.com/Li-En-Good/VISTA (10.5281/zenodo.4717251). These authors contributed equally: Chenxi Qian, Kun Miao. Author Contributions: L.W. conceived the study and supervised the project. C.Q., K.M., and L.W. designed the experiments and analyzed the data. C.Q. characterized the technical aspects of VISTA. C.Q., K.M., X.C., and J.D. performed the experiments. L.-E.L. performed U-Net training for multiplexed VISTA. C.Q., K.M., and L.W. wrote the paper with input from all the authors. The authors declare no competing interests. Peer review information: Nature Communications thanks Sang-Hee Shim and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Attached Files

Published - s41467-021-23951-x.pdf

Submitted - 2021.01.08.425961v1.full.pdf

Supplemental Material - 41467_2021_23951_MOESM1_ESM.pdf

Supplemental Material - 41467_2021_23951_MOESM2_ESM.pdf

Supplemental Material - 41467_2021_23951_MOESM3_ESM.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 23, 2023