Automatic Detection of Subcellular-Scale Metabolic Dynamics via Spontaneous-Stimulated Raman Spatial Colocalization

Cellular structural heterogeneity and low intrinsic contrast in label-free bright-field imaging hinder accurate localization of subcellular structures in nearly transparent specimens, thereby compromising both the precision and reproducibility of spontaneous Raman microscopy. Herein, we present a novel dual-modality, label-free three-dimensional (3D) automatic cell analysis platform that integrates spontaneous and stimulated Raman scattering (SRS) modes for morphological characterization and chemical mapping. The spatial distribution of specific molecules within cells was acquired via SRS 3D rapid imaging within 1 min. Subsequently, utilizing submicron positioning, spontaneous Raman spectroscopy was guided to automatically collect broadband, high spectral resolution Raman spectra spanning the fingerprint to high wavenumber regions, thereby obtaining the molecular component information on the specified intracellular organelles. We propose a novel method to measure the axial positioning consistency of spontaneous Raman detection. For the cell identification approach integrating Raman spectroscopy and machine learning, we demonstrate that enhanced axial positioning accuracy and sampling consistency of the system significantly improve the performance of the trained cell discrimination model relative to conventional Raman microscopy, achieving a cell identification accuracy over 98%. Using this platform, Raman spectroscopy based metabolic dynamics analysis of lipid droplet core components revealed that, under optimal culture conditions, lipid droplet cores in cell line samples were predominantly composed of the unsaturated oleate. In contrast, lipid droplet cores in clinical sample cells contained not only oleate but also a significant amount of the palmitate. Furthermore, the cholesterol ester content in lipid droplets within white blood cells of leukemia patients is significantly higher compared to that in normal samples. Deuterium labeling validated the de novo synthesis of oleate in lipid droplets of cell lines.

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Publication Details

Journal
Analytical Chemistry
Published
2026-02-13
DOI
https://doi.org/10.1021/acs.analchem.5c05866
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
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article

Automatic Detection of Subcellular-Scale Metabolic Dynamics via Spontaneous-Stimulated Raman Spatial Colocalization

Hongquan Zhu, Siqi Wang, Pei Wang, Mingshu Miao et al.
Analytical Chemistry
Spectroscopy Techniques in Biomedical and Chemical Research
article

Automatic Detection of Subcellular-Scale Metabolic Dynamics via Spontaneous-Stimulated Raman Spatial Colocalization

Hongquan Zhu, Siqi Wang, Pei Wang, Mingshu Miao, Sujun Gao, Mingbo Chi, Xing Chen, Yihui Wu, Ming Zhang
article en

Abstract

Cellular structural heterogeneity and low intrinsic contrast in label-free bright-field imaging hinder accurate localization of subcellular structures in nearly transparent specimens, thereby compromising both the precision and reproducibility of spontaneous Raman microscopy. Herein, we present a novel dual-modality, label-free three-dimensional (3D) automatic cell analysis platform that integrates spontaneous and stimulated Raman scattering (SRS) modes for morphological characterization and chemical mapping. The spatial distribution of specific molecules within cells was acquired via SRS 3D rapid imaging within 1 min. Subsequently, utilizing submicron positioning, spontaneous Raman spectroscopy was guided to automatically collect broadband, high spectral resolution Raman spectra spanning the fingerprint to high wavenumber regions, thereby obtaining the molecular component information on the specified intracellular organelles. We propose a novel method to measure the axial positioning consistency of spontaneous Raman detection. For the cell identification approach integrating Raman spectroscopy and machine learning, we demonstrate that enhanced axial positioning accuracy and sampling consistency of the system significantly improve the performance of the trained cell discrimination model relative to conventional Raman microscopy, achieving a cell identification accuracy over 98%. Using this platform, Raman spectroscopy based metabolic dynamics analysis of lipid droplet core components revealed that, under optimal culture conditions, lipid droplet cores in cell line samples were predominantly composed of the unsaturated oleate. In contrast, lipid droplet cores in clinical sample cells contained not only oleate but also a significant amount of the palmitate. Furthermore, the cholesterol ester content in lipid droplets within white blood cells of leukemia patients is significantly higher compared to that in normal samples. Deuterium labeling validated the de novo synthesis of oleate in lipid droplets of cell lines.

Analytical ChemistryVol. 98(7)
Jilin University (CN), Chinese Academy of Sciences (CN), Changchun Institute of Optics, Fine Mechanics and Physics (CN), Advanced Optical Systems (United States) (US), Jilin Medical University (CN), Centre Hospitalier de Béthune (FR), Second Affiliated Hospital of Jilin University (CN), First Bethune Hospital of Jilin University (CN), University of Chinese Academy of Sciences (CN)
Gender equality
Openalex Percentile: Top 62%
Spectroscopy Techniques in Biomedical and Chemical Research
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