Science AdvancesBioengineeringPeer-reviewed journal article
Broadband electronic resonance coherent anti-Stokes/Stokes Raman scattering microscopy
Yusuke MurakamiiD, Minori MasakiiD, Norihide Sagami, Ikuto Eshima, Ryosuke OketaniiD, Masashi YanagisawaiD, Hideaki KanoiD, Sakiko HonjohiD, Kotaro HiramatsuiD
Abstract
Coherent Raman imaging (CRI) enables label-free chemical imaging based on intrinsic molecular vibrations, but its applicability is often limited by low sensitivity, hindering the detection of low-abundance biomolecules. While electronic resonance significantly enhances sensitivity, most resonance CRI implementations rely on narrowband excitation and/or detection, which limits spectral coverage and makes it difficult to distinguish target molecules from backgrounds. Here we address these challenges by developing broadband electronic-resonance coherent anti-Stokes/Stokes Raman scattering (BER-CARS/CSRS) microscopy. We show that BER-CARS/CSRS enables highly sensitive, label-free imaging of endogenous chromophores with broad spectral coverage of the entire fingerprint region. Specifically, we captured time-lapse images across the fingerprint region, visualizing low-abundance cytochromes alongside abundant biomolecules (lipids, proteins, nucleic acids) in living HEK293 cells. Furthermore, we applied the method to complex biological tissues, mapping distinct distributions of cytochromes in mouse brain slices, highlighting their characteristic localizations in the cortex and the cerebral ventricle wall. Our results demonstrate that BER-CARS/CSRS is a powerful platform for highly sensitive chemical imaging, from large-area tissue mapping to organelle-level dynamics. By coupling resonance enhancement with broadband fingerprinting, BER-CARS/CSRS enables dynamic, label-free phenotyping of mitochondrial and physiological states from single cells to tissues, opening a path to quantitative, slide-scale chemical histopathology and intraoperative margin assessment.
- organism: mouse
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: spatial domain detection
- research focus: imputation
- research focus: dimensionality reduction
- approach: microscopy & imaging analysis
- Citations
- 1
- FWCI
- 2.72
- Field percentile
- top 19%
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