Gu Chenlei, Martella Davide Alessandro, Rose Leor Ariel, Rouatbi Nadia, Wang Cong, Zam Alaa, Caprettini Valeria, Jensen Magnus, Liu Shiyue, Hagemann Cathleen, Memdouh Siham, Serio Andrea, Abbate Vincenzo, Al-Jamal Khuloud T, Parsons Maddy, Bergholt Mads S, Brennan Paul M, Zaritsky Assaf, Chiappini Ciro
Centre for Craniofacial and Regenerative Biology, King's College London, London, UK.
London Centre for Nanotechnology, King's College London, London, UK.
Nat Nanotechnol. 2025 Jun 16. doi: 10.1038/s41565-025-01955-8.
Spatial biology provides high-content diagnostic information by mapping the molecular composition of tissues. However, traditional spatial biology approaches typically require non-living samples, limiting temporal analysis. Here, to address this limitation, we present a workflow using porous silicon nanoneedles to repeatedly collect biomolecules from live brain tissues and map lipid distribution through desorption electrospray ionization mass spectrometry imaging. This method preserves the integrity of the original tissue while replicating its spatial molecular profile on the nanoneedle substrate, accurately reflecting lipid distribution and tissue morphology. Machine learning analysis of 23 human glioma biopsies demonstrated that nanoneedle sampling enables the precise classification of disease states. Furthermore, a spatiotemporal analysis of mouse gliomas treated with temozolomide revealed time- and treatment-dependent variations in lipid composition. Our approach enables non-destructive spatiotemporal lipidomics, advancing molecular diagnostics for precision medicine.
空间生物学通过绘制组织的分子组成来提供高内涵诊断信息。然而，传统的空间生物学方法通常需要非活体样本，限制了时间分析。在此，为解决这一限制，我们提出了一种工作流程，利用多孔硅纳米针从活脑组织中反复收集生物分子，并通过解吸电喷雾电离质谱成像来绘制脂质分布。该方法在保留原始组织完整性的同时，在纳米针基底上复制其空间分子图谱，准确反映脂质分布和组织形态。对23例人类胶质瘤活检样本的机器学习分析表明，纳米针采样能够精确分类疾病状态。此外，对接受替莫唑胺治疗的小鼠胶质瘤进行的时空分析揭示了脂质组成随时间和治疗的依赖性变化。我们的方法实现了无损时空脂质组学，推动了精准医学的分子诊断。
