Bibliometric analysis of global research on human organoids

Heliyon. 2024 Mar 10;10(6):e27627. doi: 10.1016/j.heliyon.2024.e27627. eCollection 2024 Mar 30.

Abstract

The emergence and rapid development of human organoids have provided the possibility to replace animal models in treating human diseases. Intelligence studies help focus on research hotspots and address key mechanistic issues. Currently, few comprehensive studies describe the characteristics of human organoid research. In this study, we extracted 8,591 original articles on organoids from the Web of Science core collection database over the past two decades and conducted intelligence analysis using CiteSpace. The number of publications in this field has experienced rapid growth in the last ten years (almost 70-fold increase since 2009). The United States, China, Germany, Netherlands, and UK have strong collaborations in publishing articles. Clevers Hans, Van Der Laan, Jason R Spence, and Sato Toshiro have made significant contributions to advancing progress in this field. Clustering and burst analysis categorized research hotspots into tissue model and functional construction, intercellular signaling, immune mechanisms, and tumor metastasis. Organoid research in highly cited articles covers four major areas: basic research (38%), involving stem cell developmental processes and cell-cell interactions; biobanking (10%), with a focus on organoid cultivation; precision medicine (16%), emphasizing cell therapy and drug development; and disease modeling (36%), including pathogen analysis and screening for disease-related genetic variations. The main obstacles currently faced in organoid research include cost and technology, vascularization of cells, immune system establishment, international standard protocols, and limited availability of high-quality clinical trial data. Future research will focus on cost-saving measures, technology sharing, development of international standards, and conducting high-level clinical trials.

Keywords: Bibliometric analysis; Biobanking; Disease modeling; Human organoid; Precision medicine.