A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Ruixin Yang; Yutong Du; Wingyan Kwan; Ranlin Yan; Qimeng Shi; Lu Zang; Zhenggang Zhu; Jianming Zhang; Chen Li; Yingyan Yu

doi:10.20892/j.issn.2095-3941.2023.0099

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids

Cancer Biol Med. 2023 Jun 30;20(7):519-536. doi: 10.20892/j.issn.2095-3941.2023.0099.

Authors

Ruixin Yang^#¹, Yutong Du^#¹, Wingyan Kwan¹, Ranlin Yan¹, Qimeng Shi¹, Lu Zang¹, Zhenggang Zhu¹, Jianming Zhang², Chen Li¹, Yingyan Yu¹

Affiliations

¹ Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.
² Institute of Translational Medicine, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201210, China.

^# Contributed equally.

PMID: 37417294
PMCID: PMC10466441
DOI: 10.20892/j.issn.2095-3941.2023.0099

Abstract

Objective: Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for testing potential drugs before clinical trials. However, the number of passages for which organoids maintain cellular vitality ex vivo remains unclear.

Methods: Herein, we constructed 55 gastric organoids from 35 individuals, serially passaged the organoids, and captured microscopic images for phenotypic evaluation. Senescence-associated β-galactosidase (SA-β-Gal), cell diameter in suspension, and gene expression reflecting cell cycle regulation were examined. The YOLOv3 object detection algorithm integrated with a convolutional block attention module (CBAM) was used to evaluate organoid vitality.

Results: SA-β-Gal staining intensity; single-cell diameter; and expression of p15, p16, p21, CCNA2, CCNE2, and LMNB1 reflected the progression of aging in organoids during passaging. The CBAM-YOLOv3 algorithm precisely evaluated aging organoids on the basis of organoid average diameter, organoid number, and number × diameter, and the findings positively correlated with SA-β-Gal staining and single-cell diameter. Organoids derived from normal gastric mucosa had limited passaging ability (passages 1-5), before aging, whereas tumor organoids showed unlimited passaging potential for more than 45 passages (511 days) without showing clear senescence.

Conclusions: Given the lack of indicators for evaluating organoid growth status, we established a reliable approach for integrated analysis of phenotypic parameters that uses an artificial intelligence algorithm to indicate organoid vitality. This method enables precise evaluation of organoid status in biomedical studies and monitoring of living biobanks.

Keywords: Gastric cancer; artificial intelligence; cellular senescence; organoids; senescence-associated β-galactosidase.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aging
Animals
Artificial Intelligence*
Cell Cycle
Cellular Senescence*
Humans
Organoids

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China (Grant Nos. 82072602 and 82173222), the Science and Technology Commission of Shanghai Municipality (Grant Nos. 20DZ2201900 and 18411953100), the Innovation Foundation of Translational Medicine of Shanghai Jiaotong University School of Medicine (Grant No. TM202001), and the Collaborative Innovation Center for Clinical and Translational Science of the Chinese Ministry of Education & Shanghai (Grant No. CCTS-2022202). Chinese Patent Application No. 202310629787.7.