Construction of a metastasis model for liver cancer spheroids to hepatobiliary organoids facilitated by holographic acoustic tweezers
Source abstract
Abstract
Constructing an in vitro model that can faithfully recapitulate cancer metastasis to surrounding tissues remains an urgent need and a significant challenge. Here, we develop holographic acoustic tweezers and create a personalized composite acoustic field for liver cancer spheroids, enabling accurate movement under the influence of acoustic radiation force. We also assess the biosafety of three-dimensional holographic acoustic fields on organoids and spheroids at the cellular and genetic levels, optimizing the parameters for manipulation. Additionally, we develop an organoid-spheroid online assembly platform based on holographic acoustic tweezers and three-dimensional cell culture technology and use this platform to construct a reliable liver cancer metastasis model with hepatobiliary organoids and liver cancer spheroids. Furthermore, we demonstrate the versatility of our model by studying the infiltration of liver cancer spheroids into normal and ethanol-treated hepatobiliary organoids. Our findings present a valuable platform for cancer metastasis research. • RNA sequencing optimizes the biosafety of 3D acoustic fields on organoids and spheroids • OSOAP is developed to accurately construct a liver cancer metastasis model • Morphological and genetic analyses confirm the liver cancer metastasis model is reliable • Ethanol-treated hepatobiliary organoids exhibit increased liver cancer spheroid infiltration Li et al. develop an organoid-spheroid online assembly platform to construct an accurate, reliable, and versatile liver cancer metastasis model. This model not only faithfully replicates the metastasis process of liver cancer but also provides a valuable platform for cancer metastasis research.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Liver physiology and pathology · Spacecraft and Cryogenic Technologies · Fibroblast Growth Factor Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
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