Full range characterization of the Raman spectra of organs in a murine model

Huang, Naiyan; Short, Michael; Zhao, Jianhua; Wang, Hequn; Lui, Harvey; Korbelik, Mladen; Zeng, Haishan

doi:10.1364/oe.19.022892

Cited by 113 publications

(79 citation statements)

References 49 publications

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“…Horsnell et al measured lymph node metastases using a hand-held Raman probe [430]. Huang et al used their Raman probe to study the entire body of a mouse [431]. Recently, Desroches et al applied their Raman probe during surgery to identify tumor in the brain [432].…”

Section: Applicationsmentioning

confidence: 99%

Raman spectroscopy: techniques and applications in the life sciences

Shipp¹,

Sinjab²,

Notingher³

2017

Adv. Opt. Photon.

268

189

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Raman spectroscopy is an increasingly popular technique in many areas including biology and medicine.It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration.

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Section: Applicationsmentioning

confidence: 99%

Raman spectroscopy: techniques and applications in the life sciences

Shipp¹,

Sinjab²,

Notingher³

2017

Adv. Opt. Photon.

268

189

View full text Add to dashboard Cite

show abstract

“…[18][19][20][21][22][23][24][25] Recent improvements in optical filters make it possible to extract small Raman shifts and directly distinguish intermolecular bonds in addition to intramolecular bonds. 26,27 Moreover, Raman peak deformations in intramolecular bonds reveal the adsorption of molecular crystals on the surfaces, 28,29 which play an important role in cocrystallization in porous materials.…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopy of Pharmaceutical Cocrystals in Nanosized Pores of Mesoporous Silica

2017

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The Raman spectroscopy of pharmaceutical cocrystals based on caffeine and oxalic acid in nanosized pores of mesoporous silica has been demonstrated at various molar amounts. The Raman peak shifts of caffeine molecules express the existence of pharmaceutical cocrystals in mesoporous silica. The molar amount dependence of the peak shifts describes that caffeine and oxalic acid cocrystallized on the surface of the nanosized pores and piled up layer by layer. This is the first report that shows the Raman spectroscopy is a powerful tool to observe the synthesis of pharmaceutical cocrystals incorporated in the nanosized pores of mesoporous silica. The results indicate a way to control the size of cocrystals on a nanometer scale, which will provide higher bioavailability of pharmaceuticals.

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“…Many techniques for guiding these surgical interventions have been explored, including mapping pre-procedure imaging to the surgical field or the integration of novel intraoperative image guidance systems [15][16][17]. Prior work has demonstrated the potential of Raman spectroscopy as an intraoperative tool for guiding resection of breast tumors [7] but applying a similar technique in the liver would be challenging due to the strong background autofluorescence that overwhelms the Raman scattered signal [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Temporal gating has also been an effective method for recovering Raman signal from liver specimens [19], but implementation requires complex and costly ultrafast laser sources that complicate in vivo application. Other groups that have reported Raman spectra directly from bulk liver specimens have either been able to recover only the most prominent peaks in the fingerprint region or have directly acknowledged the challenge autofluorescence presents [18,24,25]. For example, Huang et al reported the feasibility of recovering the spectral signature from high-wavenumber regions; however, overwhelming autofluorescence intensity precluded the direct collection of Raman fingerprint signals with sufficient signal to noise ratios for interpretation [18].…”

Section: Introductionmentioning

confidence: 99%

“…Other groups that have reported Raman spectra directly from bulk liver specimens have either been able to recover only the most prominent peaks in the fingerprint region or have directly acknowledged the challenge autofluorescence presents [18,24,25]. For example, Huang et al reported the feasibility of recovering the spectral signature from high-wavenumber regions; however, overwhelming autofluorescence intensity precluded the direct collection of Raman fingerprint signals with sufficient signal to noise ratios for interpretation [18]. Unfortunately, the resulting high-wavenumber spectra have not provided as rich a Raman signature for classification of tissue types in comparison to those from the fingerprint region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

Pence

Patil

Lieber³

et al. 2015

Biomed. Opt. Express

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Abstract:Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm −1 ); lactic acid (838 cm −1 ); collagen (873 cm −1 ); and nucleic acids (1485 cm −1 ). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

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Full range characterization of the Raman spectra of organs in a murine model

Cited by 113 publications

References 49 publications

Raman spectroscopy: techniques and applications in the life sciences

Raman spectroscopy: techniques and applications in the life sciences

Raman Spectroscopy of Pharmaceutical Cocrystals in Nanosized Pores of Mesoporous Silica

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

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