Raman spectroscopy: techniques and applications in the life sciences

Dustin W. Shipp; Faris Sinjab; Ioan Notingher

doi:10.1364/AOP.9.000315

Advances in Optics and Photonics
Vol. 9,
Issue 2,
pp. 315-428
(2017)
•https://doi.org/10.1364/AOP.9.000315

Raman spectroscopy: techniques and applications in the life sciences

Dustin W. Shipp, Faris Sinjab, and Ioan Notingher

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Dustin W. Shipp, Faris Sinjab, and Ioan Notingher, "Raman spectroscopy: techniques and applications in the life sciences," Adv. Opt. Photon. 9, 315-428 (2017)

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- Microscopy
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About this Article
History
- Original Manuscript: January 12, 2017
- Revised Manuscript: March 29, 2017
- Manuscript Accepted: April 5, 2017
- Published: June 9, 2017

Abstract

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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$D_{\infty h}$	$E$	$2 C_{\infty}$	$\infty σ_{v}$	$i$	$2 S_{\infty}$	$\infty C_{2}$
$A_{1 g}$	1	1	1	1	1	1
$A_{2 g}$	1	1	−1	1	1	−1
$E_{1 g}$	2	$2 \cos (ϕ)$	0	2	$- 2 \cos (ϕ)$	0
$E_{2 g}$	2	$2 \cos (2 ϕ)$	0	2	$2 \cos (2 ϕ)$	0
$E_{3 g}$	2	$2 \cos (3 ϕ)$	0	2	$- 2 \cos (3 ϕ)$	0
$A_{1 u}$	1	1	1	−1	−1	−1
$A_{2 u}$	1	1	−1	−1	−1	1
$E_{1 u}$	2	$2 \cos (ϕ)$	0	−2	$2 \cos (ϕ)$	0
$E_{2 u}$	2	$2 \cos (2 ϕ)$	0	−2	$- 2 \cos (2 ϕ)$	0
$E_{3 u}$	2	$2 \cos (3 ϕ)$	0	−2	$2 \cos (3 ϕ)$	0

$D_{\infty h}$	Linear	Rotation	Quadratic
$A_{1 g}$	—	—	$x^{2} + y^{2}$ , $z^{2}$
$A_{2 g}$	—	$R_{z}$	—
$E_{1 g}$	—	$R_{x}$ , $R_{y}$	$x^{2}$ , $y^{2}$
$E_{2 g}$	—	—	$x^{2} - y^{2}$ , $x y$
$E_{3 g}$	—	—	—
$A_{1 u}$	$z$	—	—
$A_{2 u}$	—	—	—
$E_{1 u}$	$x$ , $y$	—	—
$E_{2 u}$	—	—	—
$E_{3 u}$	—	—	—

Band Location ( ${cm}^{- 1}$ )	Assignment	References
669	Thymine, Guanine	[69]
723–728	C─N head group in Adenine	[69–74]
763	Pyrimidines (Cytosine, Thymine, Uracil)	[74–76]
782–792	Uracil, Cytosine, Thymine ring breathing; O-P-O symmetric stretch	[69,70,72,73,76–79]
813	RNA, A-type DNA	[77,80,81]
828	O-P-O	[69,72,73]
898	Adenine, nucleotide backbone	[69,70,72]
1084–1095	${PO}_{2}$ stretch	[69,71,77,82,83]
1173–1180	Cytosine, Guanine, Adenine	[79,84,85]
1252–1254	Cytosine, Guanine, Adenine	[69,70,72,84,86]
1304–1342	Adenine, Guanine	[69,70,72,73,78,84,87–91]
1369	Thymine, Adenine, Guanine	[70,72]
1487	Guanine, Adenine	[72]
1510	Adenine	[72]
1578	Guanine, Adenine	[69,72]
1659–1671	Thymine, Guanine, Cytosine	[70,71,74,75,77,79,91–97]

Band Location ( ${cm}^{- 1}$ )	Assignment	References
500–550	Disulfide S─S stretch (conformation dependent)	[78,98,100]
755–760	Trp ring br.	[69,78,98,100–102]
828–830	Tyr out of plane ring br.	[69,78,98,100–103]
850–855	Pro C─C stretch (Collagen); Tyrosine ring br.	[73,101,104];[98,100–102]
935–937	Pro C─C stretch (Collagen); C─C backbone stretch ( $α$ -helix)	[73,104]; [69,78,101]
1000–1006	Phe symmetric ring br.	[69,78,98,100,101,101–103]
1014	Trp symmetric ring br.	[98,100,102]
1030–1033	Phe C-H in plane bend	[98,100]
1066; 1080–1083; 1125–1128	C─N stretch	[69,78,101]
1155–1158	C─C/C─N stretch	[69,101]
1170–1177	Tyr C-H in plane bend	[69,78,101]
1206–1214	Phe, Trp $C - C_{6} H_{5}$ stretch	[69,78,100,101,103]
1225–1280	Amide III (random coil 1225-1240; $β$ -pleated sheet 1240-1260; $α$ -helix 1260-1280)	[69,78,98,100,101]
1338–1340	$C_{α} - H$ deformation; Trp	[69,78,101,103];[100,101]
1446–1449	$C - H_{2}$ bending	[69,78,101,103]
1600–1610	Phe, Tyr $C = C$ in plane bending	[69,78,100,101,103]
1614–1617	Tyr, Trp $C = C$ stretch	[69,78,100,101,103]
1645–1680	Amide I ( $α$ -helix 1645-1660; $β$ -pleated sheet 1660-1670; random coil 1665-1680)	[98,100,101]

Band Location ( ${cm}^{- 1}$ )	Assignment	References
719	${CN}^{+} C$ (choline)	[105]
887	${CH}_{2}$ deformations	[90]
955–975	${CH}_{3}$ deformations	[73,90,91]
985	C─C head groups	[69]
1030–1130	skeletal C─C stretches (cis 1030–1040; chain trans 1055–1066; chain random 1080–1085; chain 1092-1098; trans 1127)	[69,70,73,77,90,91,93,106]
1254–1284	═CH deformations	[69–72,77]
1295–1305	${CH}_{2}$	[69,70,72,73,90,91]
1310–1315	${CH}_{3} {CH}_{2}$ twisting	[70,77,78,85,107]
1336–1341	CH deformations	[70,71,73]
1365–1380	${CH}_{3}$ symmetric stretch	[69,70,85,91,108]
1440–1460	${CH}_{2}$ scissors	[69,70,70,71,73,90,91]
1645–1660	$C ═ C$ cis stretch	[69–71,73–75,77,79,91–97]
1720–1750	$C ═ O$ esters	[69,77,91,94,109,110]

Band Location ( ${cm}^{- 1}$ )	Assignment	Molecule	References
2560–2600	S─H	proteins	[98]
2850–2890	${CH}_{2}$ stretch (symmetric 2850; asymmetric 2890)	lipids	[94,122–126]
2935	chain end ${CH}_{3}$ stretch	lipids	[94,122,124]
2940	symmetric ${CH}_{3}$ stretch	proteins	[123,124,126–128]
2960	${CH}_{3}$ stretch	nucleic acids	[129]
2980 (shoulder)	asymmetric ${CH}_{3}$ stretch	proteins	[124,126,128]
3010	unsaturated ═CH stretch	lipids	[94,122,125]
3057–3090	Amide B	proteins	[98,101]
3250	symmetric OH stretch	water	[126]
3280–3308	Amide A	proteins	[98,101]
3400	asymmetric OH stretch	water	[123,125–127]

Technique	Main Advantages	Challenges	Sample Preparation	Full Spectrum
Spontaneous Raman microscopy	Spectral detail, classification	Slow	No	Yes
Resonance Raman spectroscopy	More signal, specific target	Only possible with some molecules	No	Yes, some bands enhanced
Coherent anti-Stokes Raman scattering (CARS)	Very fast imaging	Costly instrumentation, signal depends quadratically on concentration	No	Possible with more complex instrumentation
Stimulated Raman scattering (SRS)	Very fast imaging, signal depends linearly on concentration	Costly instrumentation	No	Possible with more complex instrumentation
Surface enhanced Raman scattering (SERS)	Greatly enhanced signal	High variability in spectra of endogenous molecules	Yes, nanoparticles	Yes, but complex selection rules cause non-standard spectra
Tip-enhanced Raman scattering (TERS)	Greatly enhanced signal, sub-diffraction limit resolution surface specific	High variability in spectra, tip durability	No, nanoparticles	Yes, but complex selection rules cause non-standard spectra
Spatially offset Raman spectroscopy (SORS)	Depth-resolved spectra from turbid media	Resolution (lateral and depth) much larger than diffraction limit	No	Yes
Transmission Raman spectroscopy	Signal from deep in turbid media	Volume analysis, no depth resolution	No	Yes
Fiber optic probes	Access to remote areas, in vivo measurements	Signal from glass fibers	No	Yes
Selective scanning Raman spectroscopy (SSRS)	Faster imaging	Possibly decreased resolution	No	Yes
Stable isotope labels	Non-invasive labeling	Limited targets	Yes, minimally invasive	Yes
Alkyne labels	Non-invasive labeling	Some labels can affect function	Yes, possibly invasive	Yes

Method	Summary	Supervised	Target	Sensitivity^a
Direct peak analysis	Peak areas indicate concentration of analyte	either	any	low
Principal component analysis (PCA)	Finds variables responsible for variance between spectra	no	none	high
Cluster analysis	Identifies groups of similar spectra	no	none	medium
Linear discriminant analysis (LDA)	Separates classes using linear boundary	yes	binary classes	low
Logistic regression	Separates classes using logarithmic boundary	yes	classes	medium
Support vector machines (SVM)	Maximizes separation between classes using higher-order boundary	yes	classes	high
Decision tree (incl. random forest)	Classifies spectra based on series of binary decisions	yes	classes	high
Partial least squares (PLS)	Identifies spectral features correlated to training values	yes	numerical value (e.g., concentration)	medium
Artificial neural network (ANN)	Calculations through a series of “neurons”	yes	classes or numerical value	high
Ant colony optimization	Paths to successful models attract subsequent paths	yes	fitness parameter	high
Genetic algorithm	Model parameters “evolve” to succeeding generations	yes	fitness parameter	high

Abstract

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Advances in Optics and Photonics