uniLIVER: a human liver cell atlas for data-driven cellular state mapping

Yanhong Wu; Yuhan Fan; Yuxin Miao; Yuman Li; Guifang Du; Zeyu Chen; Jinmei Diao; Yu-Ann Chen; Mingli Ye; Renke You; Amin Chen; Yixin Chen; Wenrui Li; Wenbo Guo; Jiahong Dong; Xuegong Zhang; Yunfang Wang; Jin Gu

doi:10.1016/j.jgg.2025.01.017

uniLIVER: a human liver cell atlas for data-driven cellular state mapping

doi: 10.1016/j.jgg.2025.01.017

a. MOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China;
b. Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China;
c. Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China;
d. Fuzhou Institute of Data Technology, Fuzhou, Fujian 350207, China;
e. Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China

基金项目:

This publication is part of the Human Cell Atlas https://www.humancellatlas.org/publications/. This work is funded by the National Key Research and Development Program of China (No. 2021YFF1200901) and the National Natural Science Foundation of China (Nos. 61721003, 62133006, and 92268104). We thank Qiuyu Lian, Qinglin Mei, Yiran Shan, Xinqi Li, Qifan Hu, Nan Yan, and Yifan Sun for their help on this work.

详细信息

通讯作者:
Yunfang Wang,E-mail:wangyf2011126@126.com

Jin Gu,E-mail:jgu@tsinghua.edu.cn

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出版历程
- 收稿日期: 2025-01-21
- 录用日期: 2025-01-22
- 网络出版日期: 2025-07-11

uniLIVER: a human liver cell atlas for data-driven cellular state mapping

a. MOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China;
b. Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China;
c. Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China;
d. Fuzhou Institute of Data Technology, Fuzhou, Fujian 350207, China;
e. Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China

Funds:

摘要

摘要:
The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://lifeome.net/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues.

Abstract:
The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://lifeome.net/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues.