A FAPα-activated MRI nanoprobe for precise grading diagnosis of clinical liver fibrosis

Jiahao Gao, Ya Wang, Xianfu Meng, Xiaoshuang Wang, Fang Han, Hao Xing, Guanglei Lv, Li Zhang, Shiman Wu, Xingwu Jiang, Zhenwei Yao, Xiangming Fang (), Jiawen Zhang () and Wenbo Bu ()
Additional contact information
Jiahao Gao: Fudan University
Ya Wang: Fudan University
Xianfu Meng: Fudan University
Xiaoshuang Wang: Fudan University
Fang Han: Fudan University
Hao Xing: Fudan University
Guanglei Lv: Yiwu Research Institute of Fudan University
Li Zhang: Tongji University School of Medicine
Shiman Wu: Fudan University
Xingwu Jiang: Fudan University
Zhenwei Yao: Fudan University
Xiangming Fang: Nanjing Medical University
Jiawen Zhang: Fudan University
Wenbo Bu: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Molecular imaging holds the potential for noninvasive and accurate grading of liver fibrosis. It is limited by the lack of biomarkers that strongly correlate with liver fibrosis grade. Here, we discover the grading potential of fibroblast activation protein alpha (FAPα) for liver fibrosis through transcriptional analysis and biological assays on clinical liver samples. The protein and mRNA expression of FAPα are linearly correlated with fibrosis grade (R2 = 0.89 and 0.91, respectively). A FAPα-responsive MRI molecular nanoprobe is prepared for quantitatively grading liver fibrosis. The nanoprobe is composed of superparamagnetic amorphous iron nanoparticles (AFeNPs) and paramagnetic gadoteric acid (Gd-DOTA) connected by FAPα-responsive peptide chains (ASGPAGPA). As liver fibrosis worsens, the increased FAPα cut off more ASGPAGPA, restoring a higher T1-MRI signal of Gd-DOTA. Otherwise, the signal remains quenched due to the distance-dependent magnetic resonance tuning (MRET) effect between AFeNPs and Gd-DOTA. The nanoprobe identifies F1, F2, F3, and F4 fibrosis, with area under the curve of 99.8%, 66.7%, 70.4%, and 96.3% in patients’ samples, respectively. This strategy exhibits potential in utilizing molecular imaging for the early detection and grading of liver fibrosis in the clinic.

Date: 2024
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DOI: 10.1038/s41467-024-52308-3

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