人工智能技术与生命组学的融合已成为关键趋势，为生命科学的创新发展提供强大动力。赵方庆团队提出人工智能驱动的空间蛋白组学技术框架，首次实现全组织切片水平的高分辨率空间蛋白质组检测，突破高通量原位组学技术瓶颈（Cell, 2025）；通过网格化微流控芯片设计，开发出高通量、大视野的空间转录组学新技术，为大规模三维组织研究及复杂转录过程分析开辟新路径（Nature Genetics, 2024）；提出全转录组可编程智能测序新技术，将全转录组捕获技术与计算机编程实时操控算法结合，实现目标转录本精确检测及无偏定量（Nature Cell Biology, 2024）；建立的单液滴水平的胞外小囊泡异质性追踪算法，为海量单细胞转录组学数据解析提供独特视角（Nature Methods, 2024）。

相关科研成果（发表论文、专利、标准等）：

1. Hu B, He R, Pang K, Wang G, Wang N, Zhu W, Shi X, Teng H, Liu T, Zhu J, Jiang Z, Zhang J, Zuo Z, Wang W, Ji P* & Zhao F*. High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning. Cell, 2025, 188(3):734-748.

2. Zhu J, Pang K, Hu B, He R, Wang N, Jiang Z, Ji P & Zhao F*. Custom microfluidic chip design enables cost-effective three-dimensional spatiotemporal transcriptomics with a wide field of view. Nature Genetics, 2024, 56:2259-2270.

3. Zhang J, Hou L, Ma L, Cai Z, Ye S, Liu Y, Ji P, Zuo Z & Zhao F*. Real-time and programmable transcriptome sequencing with PROFIT-seq. Nature Cell Biology, 2024, 26:2183-2194.

4. He R, Zhu J, Ji P* & Zhao F*. SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes. Nature Methods, 2024, 21:259-266.