The 3D Revolution in Cancer Discovery-Reference-Cited by-同舟云学术

The 3D Revolution in Cancer Discovery

Published:2024-04-04 Issue:4 Volume:14 Page:625-629
ISSN:2159-8274
Container-title:Cancer Discovery
language:en
Short-container-title:

Author:

Wang Linghua¹²^ORCID,Li Mingyao³^ORCID,Hwang Tae Hyun⁴^ORCID

Affiliation:

1. 1Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.

2. 2UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.

3. 3Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.

4. 4Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida.

Abstract

Summary: The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Publisher

American Association for Cancer Research (AACR)

Link

https://aacrjournals.org/cancerdiscovery/article-pdf/14/4/625/3434734/625.pdf

Reference15 articles.

1. The dawn of spatial omics;Bressan;Science,2023

2. Methods and applications for single-cell and spatial multi-omics;Vandereyken;Nat Rev Genet,2023

3. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer;Lin;Cell,2023

4. CODA: quantitative 3D reconstruction of large tissues at cellular resolution;Kiemen;Nat Methods,2022

5. VT3D: a visualization toolbox for 3D transcriptomic data;Guo;J Genet Genomics,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unlocking the power of spatial omics with AI;Nature Methods;2024-08

2. Noninvasive time-lapse 3D subcellular analysis of embryo development for machine learning-enabled prediction of blastocyst formation;2024-05-08

3. COEXIST: Coordinated single-cell integration of serial multiplexed tissue images;2024-05-07