Stimulated Raman scattering microscopy for rapid brain tumor histology

Yang, Yifan; Chen, Lingchao; Ji, Minhe

doi:10.1142/s1793545817300105

Cited by 22 publications

(15 citation statements)

References 54 publications

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“…1F. 7 Technical details of the methods behind this process are summarised in two excellent reviews from Camp et al, 8 and Yang et al, 9 whereas this review will update on the current clinical applications of SRH.…”

Section: Kristel Sepp Received Her Mchem In Medicinal and Biologicalmentioning

confidence: 99%

Recent advances in the use of stimulated Raman scattering in histopathology

Lee

Herrington

Ravindra

et al. 2021

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Section: Kristel Sepp Received Her Mchem In Medicinal and Biologicalmentioning

confidence: 99%

Recent advances in the use of stimulated Raman scattering in histopathology

Lee

Herrington

Ravindra

et al. 2021

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“…SRS microscopy has also demonstrated great potential for brain tumor detection. [106][107][108] In recent studies, SRS was used to differentiate human glioblastoma multiforme (GBM) tumors from non-neoplastic in mouse models. [109] A classifier regarding the protein/lipid ratio, axonal density, and cellularity was proposed to detect tumor infiltration with a sensitivity of 97.5% and specificity of 98.5%, which showed great potential in fast and accurate intraoperative diagnosis.…”

Section: Cancer Diagnosismentioning

confidence: 99%

Review of Stimulated Raman Scattering Microscopy Techniques and Applications in the Biosciences

Shen

et al. 2020

Advanced Biology

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elastically scattered photons) featuring time-consuming spectral integration and hence a poor acquisition speed (several seconds per line or minutes per frame). This weakness constitutes a large obstacle for real-time monitoring of dynamic events in organisms. Recently, several variations of Raman spectroscopy have been developed to enhance the sensitivity of Raman spectroscopy and dynamic processes of biological samples, including coherent Raman scattering (CRS) spectroscopy and surfaceenhanced Raman spectroscopy (SERS). SERS involves a plasmonic effect where molecules absorbed on a rough metal surface can result in high Raman scattering intensities by increasing the incident electric field. [5] For SERS technology, a SERS substrate must be introduced to enhance the detection sensitivity, and the properties of the substrate (the composition, size, shape and aggregation degree of nanoparticles) affect the shape of the SERS spectra. In addition, only gold, silver, copper, and a few rare alkali metals (such as lithium and sodium) have strong SERS effects, and it is necessary to effectively solve the biocompatibility and safety issues in the preparation of nanomaterials. With the development of laser and nonlinear optics, CRS microscopy has also been demonstrated to break the speed limit for vibrational imaging. [6] CRS has two major forms: coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS); [7] both techniques can dramatically increase the Raman signal by a few orders of magnitude and hence are able to achieve video-rate molecular imaging in vivo. [8,9] CARS and SRS processes often exist in a sample concurrently because of the same excitation conditions (spatiotemporal overlap of the pump and Stokes beam) with a frequency difference (Raman shift) matching the molecular vibrational frequency. However, CARS is an optical parametric process accompanied by a nonresonant part originating from fourwave mixing processes. This intrinsic nonresonant background results in a deviation or even distortion of the Raman spectrum. SRS is a direct process of photon-vibration energy transfer: in brief, the interaction between the electron cloud of a sample and the photons of two lasers creates an induced dipole moment within the molecule based on its polarizability, resulting in a pump energy loss (stimulated Raman loss, SRL) and a Stokes energy increase (stimulated Raman gain, SRG). The Raman signal can, therefore, be deduced from SRL or SRG, resulting in a high-fidelity Raman spectrum free from the nonresonant background. Moreover, the SRS signal exhibits Stimulated Raman scattering (SRS) microscopy is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds. Featuring high speed, high resolution, high sensitivity, high accuracy, and 3D sectioning, SRS microscopy has made tremendous progress toward biochemical information acquisition, cellular function investigation, and label-free medical diagnosis in the biosciences. In this review, the principle of ...

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“…However, Raman spectroscopy is limited by the weak signal. 14–17 Infrared absorbance spectroscopy has routinely demonstrated the ability to differentiate tissues relevant to disease 18–22 without significantly perturbing the sample, 23 providing the potential for augmenting traditional histology. Infrared spectroscopy can also probe changes associated with abnormal tissue that are difficult to determine using histology alone.…”

Section: Introductionmentioning

confidence: 99%

Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

et al. 2019

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Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FTIR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes, and can bypass the need for many chemical labels. FTIR classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map infrared spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FTIR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FTIR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.

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Stimulated Raman scattering microscopy for rapid brain tumor histology

Cited by 22 publications

References 54 publications

Recent advances in the use of stimulated Raman scattering in histopathology

Recent advances in the use of stimulated Raman scattering in histopathology

Review of Stimulated Raman Scattering Microscopy Techniques and Applications in the Biosciences

Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning

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