Zhang Yijie, Huang Luzhe, Pillar Nir, Li Yuzhu, Li Yuhang, Migas Lukasz G, Van de Plas Raf, Spraggins Jeffrey M, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA 90095, USA.
Sci Adv. 2025 Aug;11(31):eadv0741. doi: 10.1126/sciadv.adv0741. Epub 2025 Aug 1.
Imaging mass spectrometry (IMS) enables untargeted, highly multiplexed mapping of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms lack microscopy-level spatial resolution and cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging, and advanced image registration to correlate/link molecular distributions with specific tissue features and cell types. We present a diffusion model-based virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with periodic acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite using IMS data with 10-fold larger pixel size. Additionally, our approach uses optimized noise sampling during the diffusion model's inference to achieve reliable and repeatable virtual staining. We believe this virtual staining method will open avenues for IMS-based biomedical research.
成像质谱（IMS）能够以无与伦比的化学特异性和灵敏度对生物组织中的分子种类进行非靶向、高度多重映射。然而，大多数IMS平台缺乏显微镜级别的空间分辨率和细胞形态对比度，因此需要后续的组织化学染色、显微成像以及先进的图像配准，以便将分子分布与特定组织特征和细胞类型相关联/联系起来。我们提出了一种基于扩散模型的虚拟组织学染色方法，该方法提高了空间分辨率，并在无标记人体组织的质谱图像中数字式地引入细胞形态对比度。对人体肾脏组织的盲测表明，无标记样品的虚拟染色图像与它们的组织化学染色对应物（采用过碘酸-希夫染色）非常匹配，尽管使用的IMS数据像素尺寸大10倍，但在识别关键肾脏病理结构方面仍显示出高度一致性。此外，我们的方法在扩散模型推理过程中使用优化的噪声采样，以实现可靠且可重复的虚拟染色。我们相信这种虚拟染色方法将为基于IMS的生物医学研究开辟道路。
