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UVA Hyperspectral Light-Sheet Microscopy for Volumetric Metabolic Imaging: Application to Preimplantation Embryo Development

Josephine Morizet, Darren Chow, Philip Wijesinghe, Erik Schartner, George Dwapanyin, Nicolas Dubost, Graham D. Bruce, Ellen Anckaert, Kylie Dunning, Kishan Dholakia

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Abstract

Cellular metabolism is a key regulator of energetics, cell growth, regeneration, and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic cofactors, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), with subcellular resolution and in a noninvasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this paper, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during preimplantation embryo development. To validate our approach, we delineate metabolic changes during preimplantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging, or significant postprocessing of data, paving the way toward clinical translation, such as in situ, noninvasive assessment of embryo viability.
Original languageEnglish
Pages (from-to)4177-4187
Number of pages11
JournalACS Photonics
Volume10
Issue number12
Early online date13 Nov 2023
DOIs
Publication statusPublished - 20 Dec 2023

Data Availability Statement

The research data supporting this publication can be accessed at https://doi.org/10.17630/48ce8571-38bd-4c53-845a-5cfa2b89aa3a.

Funding

This work was supported by funding from the UK Engineering and Physical Sciences Research Council (EP/P030017/1, EP/R004854/1), the Australian Research Council (FL210100099), the National Health and Medical Research Council (APP2003786), and the European Union’s Horizon 2020 research and innovation program under the H2020 FETOPEN project “Dynamic” (EC-GA 863203) and “Proscope” (871212). K.R.D is supported by a Hospital Research Foundation Fellowship (Midcareer fellowship C-MCF-58-2019) and a Future Making Fellowship (University of Adelaide).

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