Nanoneedles enable spatiotemporal lipidomics of living tissues.

Journal: Nature nanotechnology
Published Date: Jun 16, 2025

Abstract

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.

Authors

  • Chenlei Gu
    Centre for Craniofacial and Regenerative Biology, King's College London, London, UK.
  • Davide Alessandro Martella
    Centre for Craniofacial and Regenerative Biology, King's College London, London, UK.
  • Leor Ariel Rose
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
  • Nadia Rouatbi
    London Centre for Nanotechnology, King's College London, London, UK.
  • Cong Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Alaa Zam
    London Centre for Nanotechnology, King's College London, London, UK.
  • Valeria Caprettini
    Centre for Craniofacial and Regenerative Biology, King's College London, London, UK.
  • Magnus Jensen
    Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.
  • Shiyue Liu
    Centre for Craniofacial and Regenerative Biology, King's College London, London, UK.
  • Cathleen Hagemann
    UK Dementia Research Institute at King's College London, London, UK.
  • Siham Memdouh
    Institute of Pharmaceutical Science, King's College London, London, UK.
  • Andrea Serio
    UK Dementia Research Institute at King's College London, London, UK.
  • Vincenzo Abbate
    Department of Analytical, Environmental and Forensic Sciences, King's College London London UK giuseppe.floresta@kcl.ac.uk vincenzo.abbate@kcl.ac.uk.
  • Khuloud T Al-Jamal
    London Centre for Nanotechnology, King's College London, London, UK.
  • Maddy Parsons
    Randall Centre for Cell and Molecular Biophysics, King's College London, London, UK.
  • Mads S Bergholt
    Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.
  • Paul M Brennan
    Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Midlothian, Edinburgh EH4 2XU, UK.
  • Assaf Zaritsky
    Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel. Electronic address: assafza@bgu.ac.il.
  • Ciro Chiappini
    Centre for Craniofacial and Regenerative Biology, King's College London, London, UK. ciro.chiappini@kcl.ac.uk.

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