Visualizing Metabolism: Lipid-Based Raman Nanoreporters as an Enabling Platform for Metabolic Imaging and Precision Medicine

Abstract Lipid-based Raman reporters represent a promising but still developing class of metabolic sensors. This review reports lipid nanoparticles and lipid conjugated inorganic nanoparticles as Raman-active systems capable of decoding cellular metabolism with spatial and chemical specificity. It is demonstrated how bioorthogonal reporters operating in the Raman-silent window were incorporated across various modalities such as spontaneous Raman, coherent anti-Stokes Raman spectroscopy, stimulated Raman scattering, surface enhanced Raman spectroscopy, spatially offset Raman spectroscopy, and tip-enhanced Raman spectroscopy. Furthermore, strategies are presented for achieving multiplexed and barcoded Raman imaging using isotopic encoding and other molecular tags. The hybrid integration of Raman with other imaging modulation such as photoacoustic, magnetic resonance imaging, and positron emission tomography is evaluated to overcome inherent limitations in penetration depth and sensitivity. Additionally, the application of Raman reporters is characterized into in vitro and in vivo studies, detailing both oncological and nononcological metabolic imaging. Finally, this review outlines pathways for technical standardization, discusses challenges in regulatory approval, and provides a forward-looking perspective on the clinical translation of Raman-based metabolic nanoprobes.

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Journal
ACS Nano Medicine
Published
2026-09-22
DOI
https://doi.org/10.1021/acsnanomed.6c00039
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
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Visualizing Metabolism: Lipid-Based Raman Nanoreporters as an Enabling Platform for Metabolic Imaging and Precision Medicine

Suresh Kumar Verma, Manas Ranjan Gartia, Anu Yadav, Joysmita Dutta et al.
ACS Nano Medicine
Spectroscopy Techniques in Biomedical and Chemical Research
article

Visualizing Metabolism: Lipid-Based Raman Nanoreporters as an Enabling Platform for Metabolic Imaging and Precision Medicine

Suresh Kumar Verma, Manas Ranjan Gartia, Anu Yadav, Joysmita Dutta, Sreejita Pal, Arghyadeep Mayur
article en

Abstract

Abstract Lipid-based Raman reporters represent a promising but still developing class of metabolic sensors. This review reports lipid nanoparticles and lipid conjugated inorganic nanoparticles as Raman-active systems capable of decoding cellular metabolism with spatial and chemical specificity. It is demonstrated how bioorthogonal reporters operating in the Raman-silent window were incorporated across various modalities such as spontaneous Raman, coherent anti-Stokes Raman spectroscopy, stimulated Raman scattering, surface enhanced Raman spectroscopy, spatially offset Raman spectroscopy, and tip-enhanced Raman spectroscopy. Furthermore, strategies are presented for achieving multiplexed and barcoded Raman imaging using isotopic encoding and other molecular tags. The hybrid integration of Raman with other imaging modulation such as photoacoustic, magnetic resonance imaging, and positron emission tomography is evaluated to overcome inherent limitations in penetration depth and sensitivity. Additionally, the application of Raman reporters is characterized into in vitro and in vivo studies, detailing both oncological and nononcological metabolic imaging. Finally, this review outlines pathways for technical standardization, discusses challenges in regulatory approval, and provides a forward-looking perspective on the clinical translation of Raman-based metabolic nanoprobes.

ACS Nano Medicine
Louisiana State University System (US), Louisiana State University (US), ITRI International (US)
Partnerships for the goals, Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Spectroscopy Techniques in Biomedical and Chemical Research
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Visualizing Metabolism: Lipid-Based Raman Nanoreporters as an Enabling Platform for Metabolic Imaging and Precision Medicine — Suresh Kumar Verma, Manas Ranjan Gartia, et al. · ACS Nano Medicine (2026) | TGRS Research Map | TGRS