Deep Learning–Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell–Cell Interaction Dynamics in the Tumor Microenvironment

Bodong Liu, Yanting Zhu, Zhenye Yang, Helen H.N. Yan, Suet Yi Leung, Jue Shi*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

The three-dimensional (3D) tumor microenvironment (TME) comprises multiple interacting cell types that critically impact tumor pathology and therapeutic response. Efficient 3D imaging assays and analysis tools could facilitate profiling and quantifying distinctive cell–cell interaction dynamics in the TMEs of a wide spectrum of human cancers. Here, we developed a 3D live-cell imaging assay using confocal microscopy of patient-derived tumor organoids and a software tool, SiQ-3D (single-cell image quantifier for 3D), that optimizes deep learning (DL)–based 3D image segmentation, single-cell phenotype classification, and tracking to automatically acquire multidimensional dynamic data for different interacting cell types in the TME. An organoid model of tumor cells interacting with natural killer cells was used to demonstrate the effectiveness of the 3D imaging assay to reveal immuno-oncology dynamics as well as the accuracy and efficiency of SiQ-3D to extract quantitative data from large 3D image datasets. SiQ-3D is Python-based, publicly available, and customizable to analyze data from both in vitro and in vivo 3D imaging. The DL-based 3D imaging analysis pipeline can be employed to study not only tumor interaction dynamics with diverse cell types in the TME but also various cell–cell interactions involved in other tissue/organ physiology and pathology.

Original languageEnglish
Pages (from-to)517-526
Number of pages10
JournalCancer Research
Volume84
Issue number4
Early online date8 Dec 2023
DOIs
Publication statusPublished - 15 Feb 2024

Scopus Subject Areas

  • Oncology
  • Cancer Research

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