Luminescence-enabled three-dimensional temperature bioimaging

Liyan Ming, Anna Romelli, José Lifante, Patrizia Canton, Ginés Lifante-Pedrola, Daniel Jaque, Erving Ximendes () and Riccardo Marin ()
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Liyan Ming: Universidad Autónoma de Madrid
Anna Romelli: Ca’ Foscari University of Venice
José Lifante: Universidad Autónoma de Madrid
Patrizia Canton: Ca’ Foscari University of Venice
Ginés Lifante-Pedrola: Universidad Autónoma de Madrid
Daniel Jaque: Universidad Autónoma de Madrid
Erving Ximendes: Universidad Autónoma de Madrid
Riccardo Marin: Universidad Autónoma de Madrid

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: Abstract Luminescence thermometry affords remote thermal readouts with high spatial resolution in a minimally invasive way. This technology has advanced our understanding of biological mechanisms and physical processes from the macro- to the submicrometric scale. Yet, current approaches only allow obtaining 2D thermal images. This aspect limits the potential of this technology, given the inherent three-dimensional nature of heat diffusion processes. Despite initial attempts, a credible method that allows extracting 3D thermal images via luminescence is missing. Here, we design such a method combining Ag2S nanothermometers and machine learning algorithms. The approach leverages the distortions in the emission spectra of luminescent nanothermometers caused by changes in temperature and tissue-induced photon extinction. The optimized neural network-based algorithm can extract this information and provide 3D thermal images of complex nanothermometer patterns. Although tested for luminescence thermometry at the in vivo level, this method has far-reaching implications for luminescence-supported 3D sensing in biological systems in general.

Date: 2025
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DOI: 10.1038/s41467-025-59681-7

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