Deep 3D Histology Powered by Tissue Clearing, Omics and AI

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2024 Jul 12

PMID 38997593

Authors

Ali Erturk

Affiliations

Soon will be listed here.

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.

Citing Articles

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PMID: 39897057 PMC: 11786909. DOI: 10.1016/j.csbj.2024.12.033.

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