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Deep 3D histology powered by tissue clearing, omics and AI

Nature Methods volume 21, pages 1153–1165 (2024)Cite this article

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Abstract

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

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Fig. 1: Optical clearing enables imaging of whole mouse bodies and human organs.
Fig. 2: Three-dimensional omics for spatial molecular maps.
Fig. 3: Conceptualizing possible approaches for 3D omics.
Fig. 4: CCFs are essential for comparing and integrating different datasets.
Fig. 5: AI tools are essential at all stages of 3D omics analysis.

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Acknowledgements

This work was supported by the Vascular Dementia Research Foundation, Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, grant 390857198), by a European Research Council Consolidator grant (GA 865323) and a Nomis Heart Atlas Project Grant (Nomis Foundation). I thank M. Elsner and J.C. Paetzold for their scientific input and for editing the manuscript.

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  1. Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany

    Ali Ertürk

  2. Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany

    Ali Ertürk

  3. School of Medicine, Koç University, İstanbul, Turkey

    Ali Ertürk

  4. Deep Piction GmbH, Munich, Germany

    Ali Ertürk

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  1. Ali Ertürk
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Correspondence to Ali Ertürk.

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A.E. is a cofounder of Deep Piction.

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Nature Methods thanks Christoph Kirst, Ludovico Silvestri, and Raju Tomer for their contribution to the peer review of this work. Primary Handling Editor: Nina Vogt, in collaboration with the Nature Methods team.

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Ertürk, A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 21, 1153–1165 (2024). https://doi.org/10.1038/s41592-024-02327-1

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