Multifocus confocal Raman microspectroscopy for fast multimode vibrational imaging of living cells

Okuno, Masanari; Hamaguchi, Hiro‐o

doi:10.1364/ol.35.004096

Cited by 89 publications

(68 citation statements)

References 14 publications

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“…For our laboratory-based instrument, the acquisition time for the autofluorescence images was 4 min (8 min for confocal fluorescence) and the integration time for each Raman spectrum was 2 s. Using these estimates, the diagnosis time for MSH was 20-60 min, which is shorter than the diagnosis time for frozen-section histopathology, currently used during MMS (45-120 min for tissue preparation and 10-15 min for diagnosis). However, because MSH diagnosis no longer is limited by time-consuming tissue sectioning and staining, the development of a prototype instrument using an optimized highspeed Raman microscope [e.g., a line-shaped laser or multifocal Raman microscope has been reported to measure 10-48 Raman spectra simultaneously (29)] and integrated confocal or structured illumination autofluorescence imaging might allow intraoperative diagnosis of tissue layers and blocks within a few minutes.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Kong

Rowlands

Varma

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

225

223

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Tissue-conserving surgery is used increasingly in cancer treatment. However, one of the main challenges in this type of surgery is the detection of tumor margins. Histopathology based on tissue sectioning and staining has been the gold standard for cancer diagnosis for more than a century. However, its use during tissue-conserving surgery is limited by time-consuming tissue preparation steps (1-2 h) and the diagnostic variability inherent in subjective image interpretation. Here, we demonstrate an integrated optical technique based on tissue autofluorescence imaging (high sensitivity and high speed but low specificity) and Raman scattering (high sensitivity and high specificity but low speed) that can overcome these limitations. Automated segmentation of autofluorescence images was used to select and prioritize the sampling points for Raman spectroscopy, which then was used to establish the diagnosis based on a spectral classification model (100% sensitivity, 92% specificity per spectrum). This automated sampling strategy allowed objective diagnosis of basal cell carcinoma in skin tissue samples excised during Mohs micrographic surgery faster than frozen section histopathology, and one or two orders of magnitude faster than previous techniques based on infrared or Raman microscopy. We also show that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. This study demonstrates the potential of this technique to provide a rapid and objective intraoperative method to spare healthy tissue and reduce unnecessary surgery by determining whether tumor cells have been removed.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Kong

Rowlands

Varma

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

225

223

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“…The excitation beam can directed onto the sampling points and directed to the spectrometer slit by a fiber bundle [160], microlens array [161], set of galvo-mirrors [162], spatial light modulator (SLM) [163], or digital micromirror device (DMD) [164]. The spectra from each point can be directed onto rows of the CCD simultaneously (i.e.…”

Section: Acquisition Timementioning

confidence: 99%

Raman spectroscopy: techniques and applications in the life sciences

Shipp¹,

Sinjab²,

Notingher³

2017

Adv. Opt. Photon.

270

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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“…In the past two decades, a number of publications have shown the successful application of Raman microspectroscopy to label-free molecular-level analysis of living cells and to discrimination of cell types. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Although Raman spectra contain rich information on molecular structure, detailed interpretation of measured spectra is often difficult because of their complexity. Each Raman spectrum obtained from space-resolved mapping measurements is usually interpreted as a superposition of several spectral components of biomolecules, as well as a background and fluorescence.…”

Section: Introductionmentioning

confidence: 99%

Molecular component distribution imaging of living cells by multivariate curve resolution analysis of space-resolved Raman spectra

Ando

Hamaguchi

2013

J. Biomed. Opt

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Abstract. Label-free Raman microspectroscopy combined with a multivariate curve resolution (MCR) analysis can be a powerful tool for studying a wide range of biomedical molecular systems. The MCR with the alternating least squares (MCR-ALS) technique, which retrieves the pure component spectra from complicatedly overlapped spectra, has been successfully applied to in vivo and molecular-level analysis of living cells. The principles of the MCR-ALS analysis are reviewed with a model system of titanium oxide crystal polymorphs, followed by two examples of in vivo Raman imaging studies of living yeast cells, fission yeast, and budding yeast. Due to the non-negative matrix factorization algorithm used in the MCR-ALS analysis, the spectral information derived from this technique is just ready for physical and/or chemical interpretations. The corresponding concentration profiles provide the molecular component distribution images (MCDIs) that are vitally important for elucidating life at the molecular level, as stated by Schroedinger in his famous book, "What is life?" Without any a priori knowledge about spectral profiles, timeand space-resolved Raman measurements of a dividing fission yeast cell with the MCR-ALS elucidate the dynamic changes of major cellular components (lipids, proteins, and polysaccharides) during the cell cycle. The MCR-ALS technique also resolves broadly overlapped OH stretch Raman bands of water, clearly indicating the existence of organelle-specific water structures in a living budding yeast cell.

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Multifocus confocal Raman microspectroscopy for fast multimode vibrational imaging of living cells

Cited by 89 publications

References 14 publications

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Raman spectroscopy: techniques and applications in the life sciences

Molecular component distribution imaging of living cells by multivariate curve resolution analysis of space-resolved Raman spectra

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