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Review
. 2018 Sep;72(1_suppl):52-84.
doi: 10.1177/0003702818791939.

Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review

Susanne Pahlow  1   2 , Karina Weber  1   2   3 , Jürgen Popp  1   2   3 , Bayden R Wood  4 , Kamila Kochan  4 , Anja Rüther  4 , David Perez-Guaita  4 , Philip Heraud  4 , Nick Stone  5 , Alex Dudgeon  5 , Ben Gardner  5 , Rohith Reddy  6 , David Mayerich  6 , Rohit Bhargava  7
Affiliations
Review

Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review

Susanne Pahlow et al. Appl Spectrosc. 2018 Sep.
No abstract available

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Figures

Figure 1
Figure 1
Measurement of SORS spectrum of the knuckle using 830 nm illumination and a hand held SORS probe. Peaks show hydroxyapatite of bone, and protein peaks. Collection time 1 s per spectrum. Unpublished data from N. Stone labs.
Figure 2
Figure 2
Sentinel lymph node identified using radiotracer and blue dye during surgery, the excised node is placed on the end of a Raman handheld probe and the spectra measured show clear differences between infiltrated and non-infiltrated (metastatic) nodes.
Figure 3
Figure 3
Schematic display of different approaches for detecting infectious diseases using Raman spectroscopy.
Figure 4
Figure 4
Schematic display of different modes for investigating bacterial cells using Raman spectroscopy.
Figure 5
Figure 5
Analysis of undifferentiated and differentiated human embryonic stem cells (hESCs) using IR imaging. (a) hESCs cytospun onto a MirrIR coated slide, overlayed with a colored grid showing the area of the slide imaged by the FPA. Each colored pixel represents the area of the slide (11 µm × 11 µm projection onto sample plane) where a single FT-IR spectrum was acquired. The color scale indicates the absorbance of the amide I protein band, used as an indication of total spectral absorbance by the sample. Arrows indicate areas of low absorbance (blue) where there were no cells, or where cells overlapped, and where absorbance is high (red). Scale bar, 100 µm. (b) The same FPA image grid as shown in (a) at full optical opacity. (c) Spectral quality testing rejects spectra that are too high or low in absorbance. Black areas indicate where spectra have been rejected from the dataset, including those areas indicated by the arrows in (a). (d) The spectral data set for hESCs in one experiment (n = 192), following quality testing, but prior to spectral pre-processing. The two prominent amide I and II protein bands at ~ 1650 and 1540 cm−1, respectively, are indicated. (e) Average spectra from the experiment in (d), for hESCs (n = 192), cells treated with cytokines BMP4/Act A for four days (n = 137), and those with the cytokine FGF for four days (n = 132). Prominent bands in the spectra have been assigned to functional group vibrations and corresponding macromolecular classes. (f) Histograms showing mean integrated areas for prominent lipid and glycogen bands (asterisked at 2920 and 1155 cm−1 in panel (e) in normalized second derivative spectra from the experiment in (d). The difference between the means of areas for both bands was significantly different between hESCs and differentiated progeny in this experiment (p < 0.001, by ANOVA). Error bars indicate standard errors of the means (hESC, n = 192; BMP4/Act A, n = 137; FGF2, n = 132).
Figure 6
Figure 6
UHCA of the multimodal image of a single Micrasterias algal cell. (a) Custer image. (b) Average infrared spectra of each class. (c) Visible image. (d) Raman average spectra of each class. Reproduced with permission from Perez-Guaita et al. Copyright 2017 Elsevier.
Figure 7
Figure 7
(a) Visible micrograph of infected RBCs demonstrating the partial darkfield effect, visualizing haemozoin deposits. (b) Chemical map generated by integrating the region between 1680 and 1620 cm−1. (c) Map of distribution of classes obtained using unsupervised hierarchical cluster analysis (UHCA), using the D-values distance algorithm for the 1700–1300 cm−1 range for five clusters. (c) UHCA map generated using the Euclidean distance algorithm for the 1700–1300 cm−1 range for five clusters. (e) Mean spectra corresponding to classes presented in (d). The purple labels correspond to bands mainly associated with haemozoin while the black labels are characteristic hemoglobin bands. (f) Mean spectra corresponding to classes presented in (e). The spectra show characteristic bands of hemoglobin, but not haemozoin.
Figure 8
Figure 8
A photomicrograph of (a) functional and (b) fixed oocytes investigated with the use of air objective (100 x/0.90 NA) in the MII stages; Integration Raman maps of a specific bands were obtained with 532 nm laser wavelength and with a sampling density of 1 µm (maximal spatial resolution equal to 0.33 µm); K-means clustering (KMC) results with the eight main classes were presented with average spectrum for each class. In (a) we have additionally presented the zoom-in of the spectral region which corresponds to the ‘‘band of life’’ for the single spectra extracted from the nucleic acids class. The Raman intensities in the region of 300–1900 cm−1 were scaled by factor of two comparing to CH-stretching region and lower region below 300 cm−1. A spectral class corresponding to substrate signal observed surrounding the oocytes was removed from the image (black pixels).
Figure 9
Figure 9
(a) The electric field distribution in a focused light beam incident of a layered sample. (b) The field is distorted significantly at the edge of a layered sample. The edge is indicated by the vertical, blue dashed line. (c) Spectral distortions due to edge effects is presented. When light is focused onto an edge, there is significant baseline variation as indicated by the green spectrum. This edge effect reduces as one moves away from the edge as the indicated by the black spectrum.
Figure 10
Figure 10
(a) The real and imaginary parts of the refractive index of PMMA are shown. (b) Spectral distortions from PMMA spheres of two different radii are presented along with the ‘‘ideal’’ spectrum (in black), which would be expected if PMMA was not spherical.
Figure 11
Figure 11
Bone and breast cancer histology classified using unsupervised and supervised machine learning. (a) Histological biopsies showing bone marrow fibrosis are shown, including a raw intensity map of the Amide I band and the resulting k-means classification results (k = 4). (b) Breast tumor biopsies (invasive ductal carcinoma) from a tissue microarray classified using a Bayesian classifier. Cancer relevant tissue types (epithelium, collagen, blood) were labeled in normal biopsies, and even simple classification methods can characterize tissues with high spatial variations, which is a common trait for tumor biopsies.
Figure 12
Figure 12
Convolutional neural networks decompose spatial features into hierarchical structures for classification. A small spatial region (including spectra) is selected as input. The network applies a series of pre-learned convolutional filters to identify a hierarchy of spatial features. The final layer is used for classification, providing a posterior probability for any cell type. Once the spatial features are learned, they can alternatively be used as input to more traditional classifiers, such as support vector machines
See this image and copyright information in PMC

References

    1. Stone N, Kendall C, Smith J, Crow P, et al. ‘‘Raman Spectroscopy for Identification of Epithelial Cancers’’. Faraday Discuss 2004. 126: 141–157. - PubMed
    1. Kendall C, Stone N, Shepherd N, Geboes K, et al. ‘‘Raman Spectroscopy, a Potential Tool for the Objective Identification and Classification of Neoplasia in Barrett’s Oesophagus’’. J. Pathol 2003. 200(5): 602–609. - PubMed
    1. Dochow S, Latka I, Becker M, Spittel R, et al. ‘‘Multicore Fiber with Integrated Fiber Bragg Gratings for Background-Free Raman Sensing’’. Opt. Express 2012. 20(18): 20156–20169. - PubMed
    1. Santos LF, Wolthuis R, Koljenović S, Almeida RM, et al. ‘‘Fiber-Optic Probes for in Vivo Raman Spectroscopy in the High-Wavenumber Region’’. Anal. Chem 2005. 77(20): 6747–6752. - PubMed
    1. Draga RO, Grimbergen MC, Vijverberg PL, Swol CFV, et al. ‘‘In Vivo Bladder Cancer Diagnosis by High-Volume Raman Spectroscopy’’. Anal. Chem 2010. 82(14): 5993–5999. - PubMed

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