Haoran Zhao , Yifan Cheng , Jiawei Li , Jiaqi Zhou , Haowei Yang , Feng Yu , Feihong Yu , Davit Khutsishvili , Zitian Wang , Shengwei Jiang , Kaixin Tan , Yi Kuang , Xinhui Xing , Shaohua Ma
{"title":"液滴工程类器官概括了具有类器官间同质性和肿瘤细胞间异质性的亲代组织转录组","authors":"Haoran Zhao , Yifan Cheng , Jiawei Li , Jiaqi Zhou , Haowei Yang , Feng Yu , Feihong Yu , Davit Khutsishvili , Zitian Wang , Shengwei Jiang , Kaixin Tan , Yi Kuang , Xinhui Xing , Shaohua Ma","doi":"10.1016/j.fmre.2022.05.018","DOIUrl":null,"url":null,"abstract":"<div><div>Organoids are expected to function as effective human organ models for precision cancer studies and drug development. Currently, primary tissue-derived organoids, termed non-engineered organoids (NEOs), are produced by manual pipetting or liquid handling that compromises organoid-organoid homogeneity and organoid-tissue consistency. Droplet-based microfluidics enables automated organoid production with high organoid-organoid homogeneity, organoid-tissue consistency, and a significantly improved production spectrum. It takes advantage of droplet-encapsulation of defined populations of cells and droplet-rendered microstructures that guide cell self-organization. Herein, we studied the droplet-engineered organoids (DEOs), derived from mouse liver tissues and human liver tumors, by using transcriptional analysis and cellular deconvolution on bulk RNA-seq data. The characteristics of DEOs are compared with the parental liver tissues (or tumors) and NEOs. The DEOs are proven higher reproducibility and consistency with the parental tissues, have a high production spectrum and shortened modeling time, and possess inter-organoid homogeneity and inter-tumor cell heterogeneity.</div></div>","PeriodicalId":34602,"journal":{"name":"Fundamental Research","volume":"4 6","pages":"Pages 1506-1514"},"PeriodicalIF":6.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Droplet-engineered organoids recapitulate parental tissue transcriptome with inter-organoid homogeneity and inter-tumor cell heterogeneity\",\"authors\":\"Haoran Zhao , Yifan Cheng , Jiawei Li , Jiaqi Zhou , Haowei Yang , Feng Yu , Feihong Yu , Davit Khutsishvili , Zitian Wang , Shengwei Jiang , Kaixin Tan , Yi Kuang , Xinhui Xing , Shaohua Ma\",\"doi\":\"10.1016/j.fmre.2022.05.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Organoids are expected to function as effective human organ models for precision cancer studies and drug development. Currently, primary tissue-derived organoids, termed non-engineered organoids (NEOs), are produced by manual pipetting or liquid handling that compromises organoid-organoid homogeneity and organoid-tissue consistency. Droplet-based microfluidics enables automated organoid production with high organoid-organoid homogeneity, organoid-tissue consistency, and a significantly improved production spectrum. It takes advantage of droplet-encapsulation of defined populations of cells and droplet-rendered microstructures that guide cell self-organization. Herein, we studied the droplet-engineered organoids (DEOs), derived from mouse liver tissues and human liver tumors, by using transcriptional analysis and cellular deconvolution on bulk RNA-seq data. The characteristics of DEOs are compared with the parental liver tissues (or tumors) and NEOs. The DEOs are proven higher reproducibility and consistency with the parental tissues, have a high production spectrum and shortened modeling time, and possess inter-organoid homogeneity and inter-tumor cell heterogeneity.</div></div>\",\"PeriodicalId\":34602,\"journal\":{\"name\":\"Fundamental Research\",\"volume\":\"4 6\",\"pages\":\"Pages 1506-1514\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fundamental Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667325822002254\",\"RegionNum\":3,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Multidisciplinary\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fundamental Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667325822002254","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Multidisciplinary","Score":null,"Total":0}
Droplet-engineered organoids recapitulate parental tissue transcriptome with inter-organoid homogeneity and inter-tumor cell heterogeneity
Organoids are expected to function as effective human organ models for precision cancer studies and drug development. Currently, primary tissue-derived organoids, termed non-engineered organoids (NEOs), are produced by manual pipetting or liquid handling that compromises organoid-organoid homogeneity and organoid-tissue consistency. Droplet-based microfluidics enables automated organoid production with high organoid-organoid homogeneity, organoid-tissue consistency, and a significantly improved production spectrum. It takes advantage of droplet-encapsulation of defined populations of cells and droplet-rendered microstructures that guide cell self-organization. Herein, we studied the droplet-engineered organoids (DEOs), derived from mouse liver tissues and human liver tumors, by using transcriptional analysis and cellular deconvolution on bulk RNA-seq data. The characteristics of DEOs are compared with the parental liver tissues (or tumors) and NEOs. The DEOs are proven higher reproducibility and consistency with the parental tissues, have a high production spectrum and shortened modeling time, and possess inter-organoid homogeneity and inter-tumor cell heterogeneity.