HuNT: A Reference Transcriptome Atlas for Human Normal Tissues

Abstract With the rapid development of transcriptomics technology, gene expression profiles of normal human tissues have been frequently utilized as valuable references to study aging and disease mechanisms. Although individual databases have been established, most human tissues remain under-sampled. Meanwhile, profiles from RNA-seq and microarray platforms must be analyzed separately due to inherent distribution bias, further weakening the statistical power of health samples. Here, we constructed a comprehensive transcriptome atlas for human normal tissues (HuNT) containing 29,769 samples covering 74 human normal tissue types or parts, 70% and 37% larger than those in the well-known GTEx database in terms of sample size and tissue diversity, respectively. More importantly, instead of separate calculation, integrative analysis has been enabled for hybrid samples across RNA-seq and microarray profiles per tissue type. Based on the largest human sample collection so far, HuNT provides the latest information on: (1) tissue-specific genes, (2) stably and highly expressed housekeeping genes for each tissue, (3) gene expression landscape across all human tissues, and (4) top correlated genes for a query in a specified tissue. A case study of four widely-acknowledged biomarkers is also illustrated in the paper. With future data accumulation, HuNT may be useful to provide not only gene expression landscapes in human normal tissues, but also benchmarks to derive interesting marker genes and even therapeutic targets. HuNT is now freely accessible at http://hunt.badd-cao.net/ .

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Publication Details

Journal
Phenomics
Published
2026-05-18
DOI
https://doi.org/10.1007/s43657-025-00250-9
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

HuNT: A Reference Transcriptome Atlas for Human Normal Tissues

Zikun Chen, Xicheng Zhang, Mou Zhang, Saifeng Mo et al.
Phenomics
Single-cell and spatial transcriptomics
article

HuNT: A Reference Transcriptome Atlas for Human Normal Tissues

Zikun Chen, Xicheng Zhang, Mou Zhang, Saifeng Mo, Kailin Tang, Mohan Chen, Xinyan Xu, Xinyue Tang, XinWen Wu, ZhiWei Cao, Yao Shi, YiHeng Wang, Jian Gao, Hao Ding, YiXin Chen, ZiHao Li
article en

Abstract

Abstract With the rapid development of transcriptomics technology, gene expression profiles of normal human tissues have been frequently utilized as valuable references to study aging and disease mechanisms. Although individual databases have been established, most human tissues remain under-sampled. Meanwhile, profiles from RNA-seq and microarray platforms must be analyzed separately due to inherent distribution bias, further weakening the statistical power of health samples. Here, we constructed a comprehensive transcriptome atlas for human normal tissues (HuNT) containing 29,769 samples covering 74 human normal tissue types or parts, 70% and 37% larger than those in the well-known GTEx database in terms of sample size and tissue diversity, respectively. More importantly, instead of separate calculation, integrative analysis has been enabled for hybrid samples across RNA-seq and microarray profiles per tissue type. Based on the largest human sample collection so far, HuNT provides the latest information on: (1) tissue-specific genes, (2) stably and highly expressed housekeeping genes for each tissue, (3) gene expression landscape across all human tissues, and (4) top correlated genes for a query in a specified tissue. A case study of four widely-acknowledged biomarkers is also illustrated in the paper. With future data accumulation, HuNT may be useful to provide not only gene expression landscapes in human normal tissues, but also benchmarks to derive interesting marker genes and even therapeutic targets. HuNT is now freely accessible at http://hunt.badd-cao.net/ .

Phenomics
Tongji University (CN), Shanghai Medical College of Fudan University (CN), Fudan University (CN), Fudan University Shanghai Cancer Center (CN), Shanghai Institutes for International Studies (CN)
National Natural Science Foundation of China, National Science and Technology Major Project, National Key Research and Development Program of China
Openalex Percentile: Top 9%
Single-cell and spatial transcriptomics
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