Nanoneedles enable spatiotemporal lipidomics of living tissues

Chenlei Gu; Davide Alessandro Martella; Leor Ariel Rose; Nadia Rouatbi; Cong Wang; Alaa Zam; Valeria Caprettini; Magnus Jensen; Shiyue Liu; Cathleen Hagemann; Siham Memdouh; Andrea Serio; Vincenzo Abbate; Khuloud T. Al-Jamal; Maddy Parsons; Mads S. Bergholt; Paul M. Brennan; Assaf Zaritsky; Ciro Chiappini

doi:10.1038/s41565-025-01955-8

Nanoneedles enable spatiotemporal lipidomics of living tissues

Chenlei Gu, Davide Alessandro Martella, Leor Ariel Rose, Nadia Rouatbi, Cong Wang, Alaa Zam, Valeria Caprettini, Magnus Jensen, Shiyue Liu, Cathleen Hagemann, Siham Memdouh, Andrea Serio, Vincenzo Abbate, Khuloud T. Al-Jamal, Maddy Parsons, Mads S. Bergholt, Paul M. Brennan, Assaf Zaritsky, Ciro Chiappini

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Nature Nanotechnology
DOIs	https://doi.org/10.1038/s41565-025-01955-8
State	Accepted/In press - 1 Jan 2025

All Science Journal Classification (ASJC) codes

Bioengineering
Atomic and Molecular Physics, and Optics
Biomedical Engineering
General Materials Science
Condensed Matter Physics
Electrical and Electronic Engineering

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Gu, C., Martella, D. A., Rose, L. A., Rouatbi, N., Wang, C., Zam, A., Caprettini, V., Jensen, M., Liu, S., Hagemann, C., Memdouh, S., Serio, A., Abbate, V., Al-Jamal, K. T., Parsons, M., Bergholt, M. S., Brennan, P. M., Zaritsky, A., & Chiappini, C. (Accepted/In press). Nanoneedles enable spatiotemporal lipidomics of living tissues. Nature Nanotechnology. https://doi.org/10.1038/s41565-025-01955-8

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title = "Nanoneedles enable spatiotemporal lipidomics of living tissues",

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.",

author = "Chenlei Gu and Martella, {Davide Alessandro} and Rose, {Leor Ariel} and Nadia Rouatbi and Cong Wang and Alaa Zam and Valeria Caprettini and Magnus Jensen and Shiyue Liu and Cathleen Hagemann and Siham Memdouh and Andrea Serio and Vincenzo Abbate and Al-Jamal, {Khuloud T.} and Maddy Parsons and Bergholt, {Mads S.} and Brennan, {Paul M.} and Assaf Zaritsky and Ciro Chiappini",

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doi = "10.1038/s41565-025-01955-8",

language = "American English",

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AU - Gu, Chenlei

AU - Martella, Davide Alessandro

AU - Rose, Leor Ariel

AU - Rouatbi, Nadia

AU - Wang, Cong

AU - Zam, Alaa

AU - Caprettini, Valeria

AU - Jensen, Magnus

AU - Liu, Shiyue

AU - Hagemann, Cathleen

AU - Memdouh, Siham

AU - Serio, Andrea

AU - Abbate, Vincenzo

AU - Al-Jamal, Khuloud T.

AU - Parsons, Maddy

AU - Bergholt, Mads S.

AU - Brennan, Paul M.

AU - Zaritsky, Assaf

AU - Chiappini, Ciro

PY - 2025/1/1

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AB - 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.

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SN - 1748-3387

JO - Nature Nanotechnology

JF - Nature Nanotechnology

ER -

Nanoneedles enable spatiotemporal lipidomics of living tissues

Abstract

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