Methods and Applications of Raman … spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biolog-ical cells

focal point reviewIWAN W. SCHIE,

CENTER FOR BIOPHOTONICS, SCIENCE, AND TECHNOLOGY,

UNIVERSITY OF CALIFORNIA–DAVIS, SACRAMENTO, CA 95817, USA

THOMAS HUSER

DEPARTMENT OF PHYSICS AND CENTER FOR BIOPHOTONICS,

UNIVERSITY OF CALIFORNIA–DAVIS, SACRAMENTO, CA 95817, USA

Methods and Applications ofRaman Microspectroscopy to

Single-Cell Analysis

Raman spectroscopy is a powerful biochemical

analysis technique that allows for the dynamic

characterization and imaging of living biolog-

ical cells in the absence of fluorescent stains. In

this review, we summarize some of the most

recent developments in the noninvasive bio-

chemical characterization of single cells by

spontaneous Raman scattering. Different in-

strumentation strategies utilizing confocal de-

tection optics, multispot, and line illumination

have been developed to improve the speed and

sensitivity of the analysis of single cells by

Raman spectroscopy. To analyze and visualize

the large data sets obtained during such

experiments, sophisticated multivariate statis-

tical analysis tools are necessary to reduce the

data and extract components of interest. We

highlight the most recent applications of single

cell analysis by Raman spectroscopy and their

biomedical implications that have enabled the

noninvasive characterization of specific meta-

bolic states of eukaryotic cells, the identification

and characterization of stem cells, and the

rapid identification of bacterial cells. We

conclude the article with a brief look into the

future of this rapidly evolving research area.

Index Headings: Intralipid; Temperature stabil-

ity; Raman scattering; Optical spectroscopy;

Optical microscopy; Inverse adding–doubling;

Optical properties.

INTRODUCTION

The ability to analyze single cellsfor their biochemical composi-tion in terms of small molecule

content, their proteomic and genomiccontent, or possibly even their epige-nome has become increasingly moreimportant as we continue to improveour understanding of cellular biology.Furthermore, because most diseasesinitially start at the level of single cells(the initial cell that stopped respondingto apoptosis signals, the initial point of abacterial or viral infection, the initialmacrophage that has begun to accumu-late excess lipids and begins to turn intoa foam cells, etc.), understanding eventsthat lead to changes in the way cellsrespond to external events, withoutharming or modifying those cells, hasbecome ever more important. Few toolsprovide a truly noninvasive and nonde-structive analytical capability at thescale of single cells. Optical microscopy,the oldest form of imaging at the

microscopic scale, has many of theseattributes: It is typically noninvasivewhen combined with fluorescent label-ing and highly sensitive detectors, itprovides single-molecule sensitivity,and it inherently provides a spatialresolution that enables the study ofliving cells, which, with novel approach-es invented during the last decade, caneven be improved down to the molecularscale. Such manifold noninvasive ana-lytical capabilities are simply not foundin any other microscopic imaging tech-nique. Electron microscopy has a simi-lar ly wide range of analyt ica lcapabilities and achieves even higherspatial resolution, but a lack of asimilarly wide range of contrast agentsand typical sample preparation stepshave limited it to providing snapshotsof cells, rather than supporting extendedlive-cell imaging.1 Secondary ion-massspectrometry, another analytical imagingtechnique, destroys cells during theimaging process.2 Another emergingtechnique is based on soft X-ray imag-ing, which provides higher spatial reso-lution than standard optical microscopy,but again, providing contrast for multi-ple species of interest simultaneously istricky, and cells typically have to be

Received 20 December 2012; accepted 6March 2013

* Authors to whom correspondence should besent. E-mail: [email protected]; [email protected]: 10.1366/12-06971

APPLIED SPECTROSCOPY OA 813

fixed in order to be imaged.3 This prettymuch leaves optical microscopy un-matched where the analysis of singlecells is concerned.

Extensive research during the lastdecade has lead to other significantadvances in optical microscopy. Theability to detect single fluorescent mol-ecules with very high spatial resolution,as well as the discovery of fluorescentproteins and the possibility to incorpo-rate them into cells through geneticfusion with other proteins of interest,has lead to novel single-cell genomicsand proteomics studies. Fluorescencemicroscopy and especially super-resolu-tion optical microscopy4–6 now enablethe visualization of subcellular struc-tures and macromolecular complexes atan unprecedented level, and is nowexpanding to dynamic studies of sub-cellular structures, e.g., vesicle traffick-ing, gene transfer , cytoskeletalrearrangements, viral transfer, etc.,which will broaden our knowledge ofcell biology drastically. All of thesedevelopments, however, are currentlybased on our ability to fluorescentlylabel cellular structures. This oftennecessitates workarounds and tricks thatavoid impacting the biological activityof the cell. Fluorescent proteins are largeconstructs that have an effect on theoverall diffusion of the protein ofinterest that they are supposed to label.7

When attached to the wrong part ofanother protein, they will also signifi-cantly interfere with biological activity,i.e., by changing association and/ordissociation constants or sterically hin-dering binding activity. Similarly, manyorganic fluorophores are toxic to cells athigh concentrations. Similar issues canbe raised for many other biologicalprocesses, especially when such smallmolecules as sugars, lipids, or ions, areinvolved. The specific identification ofthese molecules by fluorescent probessignificantly interferes with the biologi-cal process of interest. In addition,fluorescence photobleaching limits theobservation time window of such sam-ples. All these issues have led research-ers around the world to try to exploreother contrast mechanisms to overcomethese problems while at the same timeallowing us to assess the biochemicalcomposition and state of cells. Here, we

review the most recent applications ofRaman microspectroscopy to the analy-sis of single cells.

Raman microspectroscopy is based onthe dispersion and detection of photonsthat are inelastically scattered by molec-ular vibrations. When a monochromaticlaser is used to probe a sample, most ofthe interacting photons undergo elasticscattering, while just a small fraction ofphotons scatters inelastically off molec-ular bonds and thereby exchanges ener-gy with the vibrational energy levels ofthe molecules in the sample. Thisprocess can result in a loss in energyof the scattered photon, termed Stokes-shifted Raman scattering, as well as again in energy of the scattered photon,termed anti-Stokes–shifted Raman scat-tering. For applications at the single-celllevel, the overall weak nature of theinelastic scattering process typicallymakes the acquisition of anti-Stokes–scattered photons unpractical, which iswhy we only discuss Stokes-shiftedRaman scattering here. The dispersionand acquisition of these scattered pho-tons results in a spectrum that containsdiscrete peaks, where each peak corre-sponds to a distinct molecular vibration.Since molecular vibrations are stronglydependent on the conformation of amolecule and its chemical environment,spectral analysis of the scattered photonscan be used to not only identify amolecular bond, but also to assess thechemical micro-environment in which itis found.

Although Raman spectroscopy canprovide substantial chemical informa-tion about single cells, the signals itprovides are very weak when comparedwith fluorescence, with typical scatter-ing cross-sections of approximately10-30 cm2. Several techniques havebeen developed to ‘‘boost’’ this signal,including resonance Raman scattering,surface-enhanced Raman scattering,8

and coherent Raman scattering.9–12 Eachone of these techniques has its own prosand cons in the analysis of single cells,which could be detailed in a separatereview of its own, but for brevity, welimit our discussion of Raman scatteringin this review solely to the use ofspontaneous Raman scattering in theanalysis of single cells. We brieflysummarize recent advances in instru-

ment development, multivariate statisti-cal analysis of complex hyperspectraldata cubes containing Raman spectra,and specific applications that demon-strate the power of Raman spectroscopyfor single-cell analysis.

RECENT DEVELOPMENTS ININSTRUMENTATION FORSINGLE-CELL RAMANSPECTROSCOPY

Confocal and Laser Tweezers Ra-man Spectroscopy. The instrumenta-tion side of Raman spectroscopy hasundergone a quantum leap since SirC.V. Raman first discovered and report-ed on it in A New Type of SecondaryRadiation13 in 1928, which was laternamed Raman scattering in his honor.Sir Raman observed the first spectra oflight that was scattered by molecularbonds by focusing sunlight into asample by using a telescope with an 18cm–diameter large lens. For filters, heused colored glasses. Even though infurther experiments he began usingmercury vapor lamps and more compactspectrometers to observe the inelasticlight-scattering properties of gases andliquids,14 it would have been impossibleto acquire Raman spectra of microscopicbiological samples, i.e., cells, with sucha setup.

The basic components, however,remain the same. The performance ofthe components, however, has changedremarkably. Thanks to the developmentof lasers, high-quality optical filters, andmodern charge-coupled device (CCD)cameras and similar detectors, Ramanspectra can now be acquired directlyfrom almost any biological sample, withlittle difficulty.15–17 To acquire Ramanspectra of biological samples, there aresome key requirements that have to besatisfied. Autofluorescence from thesample and the sample substrate has tobe minimized or creative ways forisolating the different signals have tobe developed, and sample damage has tobe avoided. The majority of Ramanmicrospectroscopy systems are designedin a confocal-like optical arrangement,which helps acquire Raman spectrafrom very small volumes, on the orderof 1 femtoliter (fL), and allows us toreject signals that are not generated inthe very focus of the laser beam. By

814 Volume 67, Number 8, 2013

focal point review

appropriately choosing the excitationsource, one can not only minimizeresidual sample autofluorescence, butalso reduce potential sample damage.Depending on the type of application, avariety of different wavelengths havebeen used in the literature, the mostcommon being 632, 647, and 785, aswell as 532 nm and very recently, 1064nm. The choice of the proper excitationwavelength has to be weighted careful-ly, because there are advantages anddisadvantages on either side. Signalintensity and detector performance in-crease with shorter wavelengths, but sodo sample autofluorescence and sampledamage. For example, it has been shownthat excitation at a wavelength of 532nm is, generally speaking, suitable forsingle-cell measurements, but it willlead to rapid cell damage, and it alsocannot be used for tissue characteriza-tion due to strong autofluorescence.Since the early days of single-cellRaman spectroscopy, Puppels et al.18

have shown that individual chromo-somes and cells degraded when probedat 514.5 nm wavelength, but canwithstand extended exposure to lightabove 660 nm. Min and colleagues19

also found that reasonably background-free Raman spectra from fresh humanlung tissue can be acquired with 1064nm excitation wavelength, but not withthe more commonly used 785 nmwavelength. Similarly, Ando et al.20

have shown that it is possible to acquirespectra of carotenoid, chlorophyll a, andphycocyanin in highly fluorescent cya-nobacterial cells by using 1064 nmexcitation. The drawback of usingwavelengths above 1 lm is, however,the significantly lower quantum effi-ciency of the detectors (e.g., an InP–InGaAsP near-infrared detector fromHamamatsu has a quantum efficiencyof only about 4% for [0.9–1.4] lm) andthe significantly reduced scatteringcross-section. Thus, excitation withinexpensive sources at 633 nm or, morecommonly 785 nm, is a compromisethat many researchers seek.

A simple confocal Raman spectros-copy setup is shown in Fig. 1a. In thisexample, the excitation beam is beingdelivered to the sample by a single-mode fiber, which produces a cleanGaussian-beam profile. The excitation

source is a 785 nm continuous-wave,fiber-coupled laser with an opticaloutput power of 50 mW, after the fiber.The beam is recollimated after exitingthe fiber by using a 25 mm focal lengthachromatic lens and expanded to adiameter of 8 mm. To remove spectralside-wing contributions from the sourceand to ensure monochromatic illumina-tion, the collimated beam is then passedthrough a narrow 785 nm bandpassfilter. The beam is reflected by a 785nm dichroic filter (DC) to the objectivelens. Signals generated in the focal planeare collected by the same objective lensand separated from the excitation sourceby DC and a sharp-edge long-pass filter.To enable confocal detection, the signalis then focused into a multimode fiberwith 105 lm core diameter so that thefiber entrance acts as a pinhole andensures confocality. The multimodefiber is connected to an imaging spec-trograph. In our experiments, we typi-cally use a 300 groove/mm grating todisperse the signal onto a back-illumi-nated, deep-depletion CCD camera. Adeep-depletion CCD camera is neededfor excitation wavelengths at 785 nmdue to its enhanced quantum efficiencyin the near-infrared, and to avoid theoccurrence of interference fringes due toreflections that appear at these wave-lengths in regular back-illuminated CCDcameras.

As mentioned earlier, there are sev-eral other obstacles to the application ofRaman spectroscopy to single cells thatneed to be overcome; autofluorescencein the substrate, e.g., glass coverslips,being one of them. Although such issuescan be avoided by using magnesiumfluoride or quartz coverslips, suchcoverslips have to be custom made andare very expensive and extremely brit-tle. An alternative way of reducingsubstrate autofluorescence and of im-proving the signal-to-noise ratio is theoperation of the confocal microscopysetup in the laser-tweezers mode. Thesetup shown in Fig. 1 can also be usedfor laser tweezers Raman spectroscopy(LTRS), when using an objective lenswith a high numerical aperture (typical-ly either a water- or oil-immersionobjective lens).

When an excitation beam with aGaussian profile is focused tightly by

such an objective lens, optical gradientforces and momentum transfer lead tothe optical manipulation of small, mi-cron-sized dielectric objects, which canbe optically trapped under the rightconditions. This allows for a wide rangeof possibilities to manipulate and char-acterize not just one, but many cells inparallel.21–23 Figure 1b illustrates thedifferent effects occurring during theoptical trapping process on a polysty-rene bead with a diameter larger than thefocal-spot diameter. When this object isilluminated by a beam that exhibitsdifferences in its intensity distribution(as indicated in the figure by thethickness of the arrows), it will experi-ence a gradient force that is dependenton the photon flux through that area.Photons change their momentum due toa change in the refractive index betweenthe medium, e.g., water, and the bead.Due to the conservation of momentum,the bead also experiences a momentumthat compensates for the change in thedirection of the photons. Hence, there isa stronger force acting on the right-handside of the particle, moving it toward thehigher beam intensity. When the sampleis illuminated by a tightly focused beamwith a radially symmetric Gaussianintensity distribution, the bead is pulledinto the center of the excitation due tothe presence of the higher beam inten-sities in the center of the Gaussian beam(Fig. 1c).

The main disadvantage of pointspectroscopy is that cells are inherentlymicroscopically small biological sys-tems with a complex substructure, andRaman spectra from individual cellularcompartments or organelles will notnecessarily reflect the overall state ofthe cell. Cell nuclei will show a richnucleic acid signature in their Ramanspectra, and spectra collected in thecytosol will exhibit high protein, lipid,and metabolite contents. Other cellularorganelles such as lipid droplets willalso produce their own signature, e.g.,that of esterified fatty acids. Because ofthis heterogeneity, it is important toacquire Raman spectra not just anindividual point within the cell, but alsoto either probe multiple spots within thecell or the entire cell. This is achieved byRaman microspectroscopy in point-scanning or line-scanning mode.


Point-Scanning and Line-ScanningRaman Microscopy. The setup of a

point-scanning Raman microspectrome-

ter is very similar to the Raman

spectroscopy setup shown in Fig. 1a.

The Raman signal is, however, acquired

by raster scanning the beam across the

sample. Raster scanning can be achieved

in multiple ways, e.g., by moving the

sample through the excitation beam, by

moving the objective lens relative to the

sample, or by scanning the excitation

beam by a pair of galvanometric mirrors

across the sample. The main disadvan-

tage of point-scanning Raman micro-

spectroscopy is that it is intrinsically

slow, because a Raman spectrum is

acquired individually from every single

point within the sample. For example, a

40 3 40 pixel image consists of a total of

1600 Raman spectra, and with an

acquisition time of 5 s per spectrum,

the entire image acquisition time is 133

min, which, of course, is not generally

suitable for living samples. One way toaccelerate the signal acquisition ofRaman spectra is to multiplex the beamgeometrically. Instead of recording aRaman signal from a single point, onecan, e.g., generate a line spot in the focalplane, and excite and detect Ramanscattered signals from the entire line.

The general layout of a line-scanningRaman microspectrometer is shown inFig. 2a. Here, we use a high-power lasersystem with an excitation wavelength of785 nm, with a single-mode fiber outputoption. After exiting the fiber, the beamis collimated and it passes a narrow 785nm laser-line bandpass filter to ensuremonochromatic excitation. An achro-matic cylindrical lens reshapes theGaussian beam with the lowest funda-mental transverse mode into a lineprofile. The cylindrical lens sits on arotation stage to ensure that the line isproperly imaged onto the entrance slit ofthe spectrometer. The line profile isimaged by an achromatic lens to theback aperture of an objective lens, whichfocuses the beam in the sample plane.The sample is held on a motorized flattop XY translation stage for invertedmicroscopes. The Raman signal gener-ated from the line focus is collected bythe same objective lens and separatedfrom the excitation source by a 785 nmdichroic long-pass filter. The Ramansignal then passes a long-pass filter andis ultimately imaged onto the entranceslit of the spectrometer. A grating isused to disperse the Raman signal fromthe line focus, and to image it in itsentirety onto a back-illuminated, deep-depletion CCD camera.

Figure 2b shows images of the CCDcamera inside the spectrometer for boththe point-scanning mode and the line-scanning mode. In the upper image, thespectrum obtained from a single point atthe sample is dispersed horizontallyacross the CCD chip. In the lowerimage, obtained in line-scanning mode,the signal is distributed along a line inthe vertical direction, which is thendispersed by the grating horizontallyacross the CCD chip. After the imageacquisition, the information is represent-ed as a hyperspectral data cube, asshown in Fig. 2c, where each pixelelement is an individual Raman spec-trum.

FIG. 1. (A) Typical confocal microscope arrangement for point-scanning Ramanspectroscopy. A detailed discussion of the setup is provided in the main text. (B) A balancebetween optical gradient forces and the transfer of photon momentum permit the opticaltrapping of single cells, as is typically used in laser tweezers Raman spectroscopy. ES =excitation source; CL = cleanup filter; L1 = lens; LP = long-pass filter; DC = dichroic filter;Obj. = objective lens; TS = translational stage; L2 = coupling lens.


focal point review

MULTIVARIATE DATAANALYSIS

Raman spectra provide a wealth ofinformation about the chemical compo-sition of a cell, which can be used toreliably identify major macromolecules,such as nucleic acids, proteins, carbo-hydrates, and lipids. This ability makesRaman spectroscopy a valuable tool forthe investigation of various biochemicalaspects of living cells and their dynamicchanges. Chemical information fromvolumes as small as 1 fL can beobtained, and this can be used to identifyeven minute changes in the chemicalcomposition on the tissue-, cellular-, andsubcellular-length scales.24–28 Raman

spectra from complex samples such assingle cells are, however, difficult toanalyze due to the simultaneous pres-ence of prodigious amounts of organicmolecules that can exhibit a largenumber of sometimes overlapping mo-lecular vibrations that are hard todistinguish. This fact is further compli-cated when large numbers of spectra arecollected by point or line scanningacross a cell. This is where multivariatestatistical data analysis shows itsstrength and provides us with a handleto better visualize the information con-tained within the spectra.

Figure 3a shows the averaged Ramanspectrum of a T-lymphocyte that wasprobed by line-scanning Raman micro-

spectroscopy. Some Raman peaks ofinterest are indicated in the figure. Theassignment of the peaks highlighted inthe figure, their associated molecularbonds, and the corresponding biomole-cules are summarized in Table 1.29–31

Figure 3b shows images of the T-lymphocyte, corresponding to the inten-sity of each highlighted Raman peak.Because of significant intensity variationsbetween the Raman peaks, each imagewas intensity scaled to better visualize thecorresponding cellular distribution of thebiomolecule. With this image, it ispossible to investigate the presence andthe location of particular molecular bondsand compare their relative signal intensi-ties. For example, one could determine

FIG. 2. (A) Expansion of a laser beam along one axis by a cylindrical lens leads to the illumination of an entire line across the sample. Whenimaged onto the entrance slit of an imaging spectrometer, the entire line can be dispersed across a CCD chip, and tens of Raman spectracan be acquired simultaneously. (B) Differences between the CCD images of Raman-scattered photons obtained by point-scanning and line-scanning Raman scattering. (C) By scanning an expanded line across the sample, the rapid acquisition of hyperspectral data cubescontaining spectral as well as temporal information on the biochemical state of a cell is facilitated. ES = excitation source; F = fiber; CL =cleanup filter; L1–5 = lens; LP= long= pass filter; CL= cylindrical lens; DC= dichroic filter; Obj.= objective lens; TS= translational stage;L2 = coupling lens.


the DNA distribution throughout the cell,based on the intensity of the 785 cm-1

ring-breathing mode or density variationsof proteins within the cell, based on the1003 cm-1 phenylalanine peak. Thismethod works well for isolated peaksthat are well characterized, but a Ramanspectrum of cells typically consists of alarge number of peaks that often overlapor partially overlap. In this case, it can bevery challenging to trace minute spectralchanges during an experiment in whichthousands of spectra are acquired withina short time. Thus, multivariate statisticalanalysis has become increasingly impor-tant in the analysis of Raman spectra.

To understand multivariate statistics,we have to remind ourselves that aRaman spectrum consisting of 1000þdata points (e.g., pixels on the CCDdetector) can also be represented as a

vector that is pointing to a specificlocation within an n-dimensional vectorspace, where n is the number of datapoints. Despite its complexity, a set ofRaman spectra has a low-rank structure,meaning it can be explained by feweruncorrelated variables, because peaksare spread across several data points,and groups of peaks can be assigned tothe same molecule, so they will scaletogether. This transformation to a re-duced set of variables is commonly doneby multivariate statistical analysis(MVA) and chemometrics. Dependingon the particular problem, differentMVA methods can be utilized. Fre-quently found methods used in theanalysis of Raman spectra are variousways of least-squares fitting procedures,principal component analysis (PCA),cluster analysis, and blind source sepa-

ration (BSS). Here, we describe some ofthese methods and provide examples oftheir applicability.

Principal Component Analysis. Awidely used MVA method to analyzeRaman spectra is PCA, because it isreadily implemented in most standardsoftware packages for data acquisitionand data analysis.32–34 A comprehensivereview of PC analysis of Raman spectracan be found elsewhere.35 PCA applies aset of linear–orthogonal transformationsto the data set, which identifies correlat-ed variables in the data and assigns themto new, uncorrelated variables, the so-called PCs. This reduces the dimensionsof the original data set by removingredundancy of information.36 Moreover,the new data set is structured based onits overall contribution (importance) tothe original data: The first PC accountsfor the largest variance within the dataset; the second PC accounts for thesecond-largest variance within the dataset, etc. Only the first few PCs containimportant information about the data;the rest is typically noise. This results ina simplified way to analyze and visual-ize complicated multidimensional datasets such as Raman spectra.

An example of three common ways todisplay information extracted by PCA isshown in Fig. 4. A scatter plot of thescores of the first two PCs is shown inFig. 4a, where the first two componentstogether account for more than 37% ofthe total data variation. The amount ofvariance accounted for by the first fewPCs is lower in Raman imaging due tothe larger data sets and the noise, whichcan increase the intraspectrum variabil-ity.35 There are a total of 1600 points inthe scatter plot, with each point corre-sponding to a single Raman spectrumfrom the same data set, as in Fig. 3.Immediately, one can see that the data isdistributed in three distinct clusters—this is critical information that wouldhave been extremely difficult to extractfrom the original data by peak analysis.Through further analytical steps (e.g., byanalyzing the ‘‘loading factors,’’ seebelow) it becomes clear that the dataclusters correspond to background spec-tra, as well as Raman spectra due tolipids, proteins and nucleic acids. A verytypical application of such scatteringplots is the distinction of spectra from

FIG. 3. (A) Raman spectrum of a living T-lymphocyte obtained in line-scanning mode.Here, all spectra were averaged to produce the average spectrum of the cell. Molecularvibrations associated with different macromolecules are indicated in the spectrum. (B)Scaled Raman images of the cell, where the intensity value of the indicated molecularvibrations highlighted in (A) is represented by the color of each pixel of the image.


focal point review

different classes of sample, e.g., celltype,37 drug exposure,38 or cell cyclestage.39

One way to determine differencesbetween the PCs is an analysis of theloading factors of the corresponding PC,Fig. 4b. The first spectrum in this figureshows the mean Raman spectrum of theT-lymphocyte, as a visual aid. The othertwo ‘‘spectra’’ show the loading factorsof the first and second PC. The loadingscan be understood as weighting func-tions that correlate the original values ofthe variables to the new values in thereduced data set, the scores. Thus, thefirst loading factor clearly shows thatpositive score values of the first PC areassociated with the actual Raman spec-trum of the cell. Accordingly, negativescores of the first PC are associated withbackground spectra. This can also beseen in the scatter plot Fig. 4a, where thebackground Raman spectra have nega-tive score values for PC1, and theRaman spectra of the cell have positivescore values for PC1. The secondloading shows the locations of peaksthat discriminate between macromole-cules in the original spectra. In this case,the presence of peaks that can beassigned to lipid vibrations in a spec-trum are associated with positive scorevalues along PC2, and peaks assigned tonucleic acid and protein vibrations areassociated with negative score valuesalong PC2. This can, again, be comparedwith the distribution of the score valuesin the scattering plot Fig. 4a.

It is also possible to apply PCA tovisualize the data in the form of animage. However, instead of reconstruct-

ing the images based on individualpeaks and their respective intensityvalues, the image is constructed basedon the PCs of the spectra for every pixel.Figure 4c shows the score image of thefirst and second PCs of the same data setas in the previous examples. This imageprovides a good visual representation ofthe information that was extracted inFigs. 4a and 4b. The first PCs show thelocation of the cell versus the back-ground, i.e., it basically shows theoutline of the cell, while the secondPCs shows the distribution of cellularmacromolecules, i.e., lipid, protein, andnucleic acid. The typical kidney-shapednucleus of a T-cell can be seen in thisimage as the object shown in blue(negative PC2 values) on the right-handside of the cell, while lipid-containingstructures are shown in red and yellowon the left-hand side of the cell. Theseimages match the previously shownimages based on the peak intensityvalues of the DNA–RNA peak at 785cm-1 and that of the lipid peak at 1441cm-1 quite well.

Cluster Analysis. Cluster analysisprovides an alternative way of recon-structing information contained in ahyperspectral data cube obtained byRaman imaging of cells.40,41 In clusteranalysis, data clusters are establishedbased on spectral similarity, e.g., Eu-clidean distances. The clusters found bythis method will be assigned a color,which can then be used to display thelocation of the constituents of eachcluster in the image. Two clusteringmethods are frequently found in Ramanspectroscopy, k-means cluster analysis

and agglomerative hierarchical clusteranalysis (AHCA).

In traditional k-means cluster analysis,the algorithm chooses at random adefined number k of initial startinglocations and subsequently comparesthe spectral distance between the cen-troid spectra and spectra assigned withthe initial cluster. After minimizing thesum of distances, a new centroidspectrum is determined, and spectra notfalling into the cluster are assigned to adifferent cluster. This process is per-formed iteratively and stops when nomore spectra can be reassigned to adifferent cluster. Once all clusters areestablished, a color code is applied torepresent the cluster groups in an image.

AHCA, on the other hand, is abottom-up clustering approach in whichevery initial spectrum is a startingcluster. On every iteration, the spectraare grouped together until only onecluster remains. The cluster groups canbe displayed in form of a dendrogram(or tree); the user can then choose thenumber of clusters of interest.

As an example for k-means clusteringapplied to Raman imaging, we used thesame data set obtained by Raman line-imaging of the T-cell that was used inthe previous examples. Figure 5a showsthe pseudocolored image of the cell,based on three, five, seven, and nineclusters. It can be seen that eachadditional cluster reveals new detailsabout the cell, but it is important to keepin mind that too many clusters canobscure important information. Figure5b shows the mean spectra for theindicated clusters for the case of k = 5.Here, we can again see that the differentclusters can be attributed to differencesin the distribution of the major cellularmacromolecules. The mean spectrum ofcluster 1 shows distinct peaks that canbe assigned to DNA–RNA and proteinvibrations, and it is most likely outliningthe cell’s nucleus, which is shown by thered in the cluster image. This observa-tion is consistent with informationextracted by PCA. The mean spectrumof cluster 2 shows a decrease in peakintensities of DNA–RNA modes, whileRaman peaks that can be assigned toprotein modes are still highly present.The mean spectra of clusters 3 and 4emphasize the phosphatidylcholine peak

TABLE I. Peak assignments for molecular Raman vibrations and related biomolecules.

Raman shift(cm-1)

Molecularbond Biomoleculea

785 O–P–O DNA backbone, DNA–RNA (U, T, C) (ring-breathing mode)1003 d(C–H) Phenylalanine (protein)1090 PO2

- Phosphate stretching (DNA)1260 CH2 Lipids, protein, amide III (in-plane deformation)1303 d(CH2) Twisting, wagging, protein, fatty acids (twisting, wagging)1342 CH DNA–RNA (G) (deformation)1444 CH2 Fatty acids, lipids, proteins (bending and scissoring)1571 DNA–RNA (G, A) (ring-breathing mode)1668 C=C Protein, a-helix of amide I, unsaturated fatty acids (stretching)1739 C=O Esters, phospholipids, esterified fatty acids (stretching)2854 CH2 Fatty acids, lipids, proteins (asymmetric stretch)2930 CH3 Proteins (symmetric stretch)

a A = adenine; C = cytosine; G = guanine; T = thymine.


even more than spectra of clusters 1 and2, highlighting a phospholipid class thatis a part of the cellular plasma mem-brane. This can also be seen by thelocation of the signal in the figure,which occurs mostly on the circumfer-ence of the cell. Cluster 5 exhibits thestrongest resemblance of a lipid Ramanspectrum. This cluster indicates mostlyneutral lipid deposits, i.e., triglyceridesthat are found in cellular lipid droplets,which can be seen by the strong signalof the triglyceride C=O peak at 1739cm-1.

Blind Source Separation. The capa-bility of Raman spectroscopy to pro-vide high chemical specificity is due to

the fact that every molecule has aunique, identifiable Raman signature,even if it is present as part of a complexmixture—the kind of environment thatis typically found in a biological cell.The Raman spectrum of a mixture ofbiomolecules is a linear combination ofRaman spectra of individual biomole-cules mixed in an unknown ratio. It ispossible to determine the relativeamount of a single compound in thatmixture by fitting Raman spectra frompure components to the experimentalspectrum by using least-squares opti-mization. This approach has beenwidely applied to determine the com-position of biomolecules in cells and

tissue by spontaneous Raman spectros-copy.25,42–44

The composition of the sample is,however, not always entirely known,and at times, it might be cumbersome orimpossible to perform an a priorichemical analysis to determine the exactcomposition of the sample. Severalmethods have been developed that canextract the ‘‘pure’’ components of asample by spectral unmixing, alsowidely known as BSS. Two methodsthat have frequently been applied toRaman spectroscopy are independentcomponent analysis45 and vertex com-ponent analysis (VCA).46 PCA is, strict-ly speaking, also a method of BSS. At

FIG. 4. PCA applied to a Raman image data set obtained by line scanning of a human lymphocyte. (A) Scatter plot of the score values ofeach spectrum for the first and second PC. The first PC separates the data, based on association with cell spectra and background spectra.The second PC separates the spectra, based on the signature of specific macromolecules, i.e., lipids, nucleic acids, and proteins. (B)Reference Raman spectrum from the cell Fig. 3a and loadings for the first and second PC. The loading for PC1 contains Raman peaks thatare positively associated with the cell; the loading for PC2 reveals Raman peaks that are positively associated with lipids and negativelyassociated with protein and nucleic acids. (C) False-colored, reconstructed images of the cell based on the score values for the first andsecond PC. Here, again, the PC1 image clearly distinguishes the cell from the surrounding background, while the PC2 image shows a clearseparation between lipid-rich areas, and protein- and nucleic acid-rich areas.


focal point review

this point, however, we only considermethods that can extract pure compo-nent spectra that represent spectra ofbiomolecules.

Vertex component analysis is anunsupervised spectral unmixing methodthat can be used to determine purecomponents from a data set. The methodis based on the assumption that if a largenumber of measurements have beenacquired, that there will be a few datapoints in the set that contain informationof pure components. The algorithmdetermines end members (pure compo-nents) that span a subspace in the datavector space. The algorithm then itera-tively projects the data orthogonal tothat subspace. The new end members arethe extreme points in the projection.47

One has to keep in mind that VCAcannot always extract pure componentspectra. In particular, this is the case

when the data set is relatively small anddoes not contain pure component spec-tra. In this case, the extracted spectrawill not necessarily reflect the real purecomponents. Once VCA has determinedthe pure components, they can be fittedin a least-squares fashion to the project-ed data set and then used to reconstructan RGB image.

Figure 6a shows the VCA image ofthe same T-cell that we used in ourprevious examples. The three purecomponents that VCA extracted wereused to reconstruct the VCA image inFig. 6b. The resulting image has a morepleasant appearance than the starkcontrast provided in the PCA image,Fig. 4c, or the cluster analysis image,Fig. 5a. As before, the extracted endmembers have high degree of similarityto spectra of nucleic acids, proteins, andesterified fatty acids. As has been

mentioned before, the end membersdetermined by VCA do not necessarilyresult in real pure components—in thiscase, nucleic acids, proteins, and ester-ified fatty acids. They can also reflect amixture of all components that isweighted toward a specific component.For example, the VCA spectrum reflect-ing cell nuclei contains not only thesignature of nucleic acids, but also thatof proteins. Similarly, the extracted endmember–reflecting proteins also containan RNA signature. The only end mem-ber that is very close to the purecomponent is the spectrum of theesterified fatty acids, mostly becausefatty acids can be found in cellular lipiddroplets at high local concentrations,resulting in a few pure spectra in theoriginal data set.

BIOLOGICAL APPLICATIONS

Cell Cycle Analysis by RamanSpectroscopy. The life cycle of a cellconstitutes the most fundamental chainof events in any living cell, characteriz-ing the phases and processes that lead toreplication or cell division in prokary-otes and eukaryotes alike (see Fig. 7).Currently, mostly fluorescence micros-copy or flow cytometry are utilized tofollow the cell cycle. This does, how-ever, require fluorescent staining ortransfection with constructs that encodefor fluorescent proteins to render cellularstructures visible that is not applicablefor cells in vivo, e.g., in an animal.Raman spectroscopy can provide aviable alternative to these establishedfluorescence-based methods, requiringno sample preparation, and providinglocalized information about a cell’snucleic acid, protein, and lipid contents.Raman spectroscopy has previouslybeen shown to provide the ability todistinguish between highly proliferatingcells such as neoplastic tumor cells andnormal cells, based on differences intheir DNA and protein content. The cellcycle itself can, on the other hand, havesignificant implications for the results ofRaman measurements. This can go as faras camouflaging subtle cellular changesor changes in a cell’s metabolism, whichcould be mistaken for increased prolif-eration. In this section, we review thecurrent state of the art on analyzing thecell cycle by Raman microspectroscopy.

FIG. 5. k-Means clustering analysis of the same lymphocyte shown previously. (A)Examples of cluster-based images obtained for an increasing number of clusters used for k-means clustering. Different, spatially distinct cellular structures emerge with an increasingnumber of clusters. (B) Mean spectra obtained for the differently colored cluster groupsindicated by numbers for the case of five clusters. The biochemical components that areidentified by cluster analysis of the spectra are indicated, and their spatial distributionwithin the cell can be seen in the five-cluster image in (A).


Short et al.48 investigated tumorigenicand nontumorigenic suspension cells intwo proliferating states; plateau cellswere defined as cells with low prolifer-ation index, and exponential cells de-fined as rapidly proliferating cells,modeling both normal tissue and solidtumors, respectively. By fitting highlyabundant biomolecules, i.e., lipid, pro-tein, DNA, and RNA signatures intotheir spectra, the group was able to showthat changes in the relative abundance ofthese biochemical compounds wereassociated with differences in theirproliferation. A thorough Raman spec-troscopic investigation of osteosarcomacells synchronized in different phases ofthe cell cycle, i.e., G0–G1, S, and G2–M,was performed by Swain et al.49 Byapplying linear discriminant analysis(LDA) to Raman spectra acquired atdifferent phases of the cell cycle, theywere able to discriminate between cellsin G0–G1 phase and cells in G2–Mphase, with 97% specificity. Matthewset al.,39 on the other hand, conducted astudy on the variability of Ramanspectra due to the influence of the cellcycle and the confluence of a cellculture. Raman spectra of resuspendedadherent cells were acquired, and theresulting data were subjected to PCAanalysis. This enabled the authors toshow that the first PC reflected changesmostly related to the variability innucleic acid and protein content relative

to the lipid content. Those changes werecorrelated to cell cycle phase-to-phasetransitions, i.e., G1–S and G2–M, andvalidated by flow cytometry. Huang andcoworkers50 investigated cell cycle–re-lated changes in molecular composition,and molecular distribution in yeast cellsin time-lapsed Raman-based imaging.Time-lapsed images based on Ramanpeaks associated with lipid and proteinvibrations were used to show thedistribution of biochemical componentsduring the G2–M phase and G1–S phase.

Single-cell Raman spectroscopy isnow frequently found in combinationwith other optical methods, especiallymultiphoton microscopy. For example,Pully et al.51 developed a hybrid two-photon excitation fluorescence (TPEF)microscope that can also acquire localRaman spectra and used this versatiledevice to investigate nucleotides inhuman bone marrow stromal stem cells.By combining these two techniques, thegroup was able to visualize cellularorganelles by using TPEF microscopy,such as the nucleus stained with anucleic acid stain (DAPI or Hoechst33 342), to then sub-sequentially acquireRaman spectra from the stained nuclei.This enabled them to make correlativestatements about the connection be-tween fluorophore-stained regions with-in the cell and their chemicalcomposition by Raman spectroscopy.Another technical extension to investi-

gate the cell cycle was developed byPliss et al.,52 who, in addition to thehybrid TPEF microscopy and Ramanspectroscopy system, added the abilityto perform coherent Raman imaging bycoherent anti-Stokes Raman-scatteringmicroscopy. This modification allowedthem to investigate changes in macro-molecular organization (fluctuations inlocal density, structural modifications)in HeLa cells in different phases of thecell cycle. Matthaus et al.,53 on the otherhand, combined Raman spectroscopywith infrared absorption spectroscopyto image the DNA and protein distribu-tion, allowing them to make directcomparisons between the techniquesand visualize different stages of mitosiswithout fluorophores. Such and similarstudies are currently being pursued byan ever-increasing number of groupsbecause of a growing interest in thelabel-free characterization of cellularchanges leading to the evolution ofcancer, as well as in their potential useas a means to distinguish and character-ize stem cells for therapeutic applica-tions.

Studies of Drug–Cell Interaction.Since Raman spectroscopy provideschemical specificity in volumes assmall as 1 fL in living cells, it is alsoa prominent tool in the evaluation of theeffects of small molecules, i.e., phar-maceutical drugs, on cells. Not only canthe presence of the drug in the cell be

FIG. 6. Image and major component spectra obtained by vertex component analysis of the same data set used in the previous figures.


focal point review

probed, but also the resulting effects ofthe drug on the cell—again, without theneed for additional, potentially interfer-ing fluorophores—to facilitate the in-vestigation of drug–cell interactions invitro as well as in vivo. In recent years,Raman scattering–based spectroscopyand microscopy has been extensivelyused to determine the effects of chemo-therapeutic agents on living eukaryoticcells, as well as antibiotics and othersmall molecules on prokaryotic cells.Raman spectroscopy is particularlyinteresting in this regard, because itenables one to follow the long-termeffects of drug treatments on cells,potentially identifying cells that avoidapoptosis and determining the mini-mum dose needed to avoid minimalresidual disease, a particular problem inthe fight against the recurrence ofcancer. Here, we summarize some of

the most recent and most significantwork in this area.

Nawaz et al.54 applied Raman spec-troscopy to determine the response oflung adenocarcinoma cells to the che-motherapeutic drug cisplatin and inves-tigated the effects of different drugconcentrations for an incubation timeof up to 96 h. In order to assess thepotential of Raman spectroscopy for thisapplication, the group was able toestablish a correlation between a classicMTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide]–based cy-totoxicity assay and Raman measure-ments of cells under the same treatmentconditions by applying partial leastsquares regression to the spectra. Theeffects of cisplatin treatment on carci-noma cells in conjunction with Ramanspectroscopy has also been studied byHuang et al.55 This group treated

nasopharyngeal carcinoma cells withcisplatin by using different drug con-centrations and obtained spectra atmultiple time points within 24 h. Theresults of the analysis showed that themeasured cellular changes were corre-lated with a decrease in Raman vibra-tional-band intensities associated withnucleic phosphodiester bonds and DNAbases, indicating a direct degradingeffect of the drug on the genome.

Though it has been established thatRaman spectroscopy is sensitive enoughto determine drug-induced changes incells at high drug concentrations, there isalso an increasing interest in researchexploring the sensitivity of Ramanspectroscopy for treatments at low oreven microdose levels. For example,Draux et al.56 investigated the effects ofnoncytotoxic doses of the anti-cancerdrug gemcitabine. In their study, humanlung carcinoma cells were exposed todrug concentrations as low as 1 nM attime points of 24 h and 48 h. Thecorresponding drug response was deter-mined based on the comparison of theRaman signature of the intact cells, aswell as chemically extracted biomole-cules such as DNA, RNA, and proteins.Their analysis showed that incubationwith 100 nM gemcitabine for 48 hresulted in a measurable DNA decreaseand measurable protein increase. Adifferent study investigated the apoptoticeffects of fluorouracil on gastric carci-noma, showing that major drug-inducedchanges are related to reduced intensitiesof vibrational bands associated withlipids, proteins, and nucleic acids.57

In contrast to these imaging-basedstudies, Moritz et al.38 applied laserLTRS to probe non-adherent humancells, which allowed them to not onlyassess these cells in a native state, butalso to significantly reduce backgroundinterference from any substrates ontowhich the cell would have adheredotherwise. They utilized this techniqueto follow the effects of doxorubicintreatment on T-lymphocytes. By obtain-ing spectra of T-cells exposed to doxo-rubicin at multiple time points over aperiod of 72 h, they were able todetermine distinct cellular spectralchanges occurring at different stages ofdrug-induced apoptosis. Similarly, Linand colleagues58 also used doxorubicin

FIG. 7. This diagram shows a simplified representation of the eukaryotic cell cycle. Duringthe different phases of the cycle the cells undergo a variety of significant changes, such asprotein increase in the G1 phase, chromosome duplication in the S phase, ensuring that thecell is ready for division in G2 phase, and process of cell division in the M phase.


to induce apoptosis in cells and studiedthe effects on extracted nuclei by usingRaman spectroscopy. While more inva-sive, this technique has the advantage ofrelating specific changes occurring inchromatin structure to their correspond-ing Raman spectra, which also allowedthem to identify effects occurring ateven lower drug concentrations. Con-ceptually, however, both techniquesprobe the cell’s nuclei, because LTRStraps human lymphocytes by their nu-cleus.59 In contrast, Buckmaster et al.60

developed a biosensor that utilizespatterned single-cell arrays in conjunc-tion with confocal Raman spectroscopyto repeatedly probe large numbers of thesame cells over an extended period. Thisformat enabled them to show that inliving human medulloblastoma cellstreated with the chemotherapeutic drugetoposide, Raman peaks associated withDNA and protein vibrations decreasedwith time.

There is, however, a particular prob-lem that occurs when studying cellularchanges based on Raman scattering in apurely spectroscopic mode, becausemost studies address specific subcellularstructures with diffraction-limited spotsizes rather than the entire cell. Cells,however, are highly complex biologicalsystems, and drug-induced changes incells are heterogeneous in nature. Thismeans that spectral changes that areobserved in Raman spectra obtainedfrom one organelle, e.g., the nucleuscan be significantly different fromchanges observed in the cytoplasm.Thus, point spectroscopy can lead topotential problems and significant vari-ations between results obtained from thesame treatment groups, and this caneasily obscure specific changes thatcould be assessed by either analyzingor imaging the entire cell. While ap-proaches to averaging Raman scatter-ing–based data over the entire volume ofa cell, such as spatial dithering of thelaser beam or shaping the beam to focusto larger volumes have been used, amore common approach to overcomesuch problems is the use of Ramanscattering in the microscopic imagingmode, resulting in the acquisition ofRaman spectra from every point withinthe cell. In early work along these lines,Uzunbajakava et al.61 developed a

confocal Raman imaging system andused it to investigate the effects of taxolon HeLa cells. Zoladel et al.62 built apoint-scanning Raman microspectrome-ter and used it for time-lapse imaging ofliving human breast cancer cells under-going apoptosis after exposure to 300lM etoposide. Hartmann et al.63 showedthat Raman images of MCF-7 breastcancer cells correlate very well to thecontrast revealed by hematoxylin andeosin staining of cells, the classical toolsused in pathology, and found thatRaman spectra from the cell nucleicould be used to distinguish betweentreated and untreated MCF-7 cells.

Identification of Cells by RamanSpectroscopy. There are several chal-lenges in modern medical diagnosticsthat can severely affect the outcome ofmedical treatment or success of asurgical procedure, in particular whenit comes to resecting tumors. A partic-ular challenge is the in vivo assessmentof the boundary between benign andmalignant tissue. In vivo Raman spec-troscopy could provide a unique oppor-tunity to guide surgeons during asurgical procedure to minimize thepossibility of recurring cancers and alsoto minimize the excision of otherwisehealthy tissue. As such, a number ofstudies have evaluated the potential ofRaman spectroscopy as a means toidentify and distinguish between differ-ent tumorigenic and normal eukaryoticcells.15,64,65 Moreover, due to their highspecificity, Raman-based methods couldalso be potentially used in the future as ameans of label-free cell sorting66 or todistinguish between different states ofdifferentiation of embryonic stemcells.67

In early work, Nothinger et al.68 usedRaman spectroscopy to discriminatebetween different bone cell types thatare commonly used in tissue engineeringof bone. By applying LDA to Ramanspectra of tumor cell lines, the authorsfound that that they could discriminatetumor cells from normal cells, with 96%specificity. In another early study, Ra-man spectroscopy was applied to ran-domly mixed cell populations of humanleukemia and human breast cancer cells,and human sarcoma and MCF-7 humancancer cells.69 This study indicated thatbased on their spectra, a good separation

between unmixed cell types can beobtained, but surprisingly the study fellshort of truly separating cells withinmixed populations. After these studies,in 2006, Rosch et al.70 showed thatRaman spectra of yeast cells providesufficient information to distinguishbetween different types of yeast cells.Chan et al.37 then used Raman spectros-copy to discriminate between fixednormal human lymphocytes and trans-formed Jurkat and Raji lymphocyte celllines, showing that they were able todetect cancer cells, with a sensitivity of98.3% and classifying cells belonging tonormal or transformed type, with asensitivity of 97.2%. Similarly, Notingh-er et al.71 used Raman spectroscopy todiscriminate between healthy and tumorbone cells, with an accuracy of 95%.After their earlier success, Chan et al.72

used LTRS in a clinical study todiscriminate between T- and B-lympho-cytes from four healthy individuals andcells from three leukemia patients,showing a positive classification of95% of cells from the healthy individ-uals and a 90% positive classification ofthe patients’ cells.

Starting in the late 2000s and afterearlier work on mouse stem cells,Raman spectroscopy was also shownable to distinguish between cells derivedfrom embryonic stem cells, progenitorcells, and differentiated cells. In earlywork on human stem cells, it was shownthat human embryonic stem cells(hESCs), hESC-derived cardiomyocytes(CM), and human fetal left-ventricularCMs, can be distinguished based ontheir Raman signature, with accuraciesof 98, 66, and 96% respectively.73 Inextensive work further analyzing thedistinctive differences between humanstem cells and their derivatives, Konor-ov et al.74–76 were able to show a majordiscriminant between the different celltypes is their glycogen content. In veryrecent work, Ghita et al.77 investigatedcytoplasmic RNA in undifferentiatedneural stem cells (NSC) as a potentialmarker to assess their degree of differ-entiation. By applying LDA, Ghita andcolleagues were able to discriminatebetween differentiated and undifferenti-ated NSC with a sensitivity of 89.4%and specificity of 96.4%. This work hassignificant potential because of the


focal point review

difficulty of obtaining fresh humanneural cells and their importance inpharmaceutical studies related to thedevelopment of novel drugs for treatingneural disorders or tumors.

If the throughput and specificity of theRaman-based evaluation of single cellscan be further improved, then rapid cellsorting in the absence of fluorescent cellmarkers or in applications where suchmarkers need to be avoided appearlikely. In a recent demonstration of thispotential, Duchow et al.78 combinedoptical trapping with cell sorting inmicrofluidic channels to discriminatebetween erythrocytes, leukocytes, acutemyeloid leukemia cells, and two types ofbreast tumor cells, with an overallaccuracy of 92.2% by using 785 nmwavelength excitation and a specificityof 94.9%, after switching to the excita-tion of Raman scattering at 514 nm.

Raman Spectroscopy of Prokaryot-ic Cells. After the initial demonstrationof single-cell Raman spectroscopy oneukaryotic cells, and after extensivework in infrared absorption spectrosco-py, it was quickly shown that Ramanscattering is also impressively sensitivewhen it comes to the identification ofprokaryotic cells and its ability todistinguish between different prokaryot-ic cells types. Similar to its potential forassessing mammalian cells, this offers awide range of possibilities, e.g., for therapid identification of contaminations onmedical devices or potential contami-nants in drugs produced in bacterial cellculture or in food processing plants. Anumber of studies have shown thatRaman scattering–based methods allowfor the assessment of the type ofcontamination, as well as the degree ofcontamination in such applications.79–84

In early work, Choo-Smith et al.85

investigated different clinically relevantcolonies of microorganisms by usingRaman and Fourier transform infraredspectroscopy at different time points andat different imaging depths. They wereable to show that the Raman signaturesfrom the various colonies were veryhomogenous when probed at 6 h afterinitiating the colony, in contrast toRaman signals obtained from coloniesat 12 and 24 h. A more careful analysisof the results obtained at the two latertime points revealed that the signals

were position and depth dependent, withthe surface layer showing high glycogencontent and deeper layer showing in-creased RNA content. Escoriza et al.86

used Raman spectroscopy to quantifyfiltered waterborne pathogenic microbialcontamination, and Pasteris et al.87 usedRaman spectroscopy to identify sulfurinclusions in sulfur-oxidizing marinebacteria. Recently, Palchaudhuri et al.88

investigated the uptake and metabolismof the five-carbon sugar alcohol xylitolby gram-positive viridian group Strep-tococcus and two gram-negative Esche-richia coli strains. In assessing Bacillusstrains, Hutsebaut et al.89 characterizedthe influence of culture conditions on theability of Raman spectroscopy to dis-criminate between Bacillus cereus, Ba-c i l l u s p u m i l u s , a n d B a c i l l u slicheniformis. They were able to showthat the identification of bacterial cellsby Raman spectroscopy at the specieslevel is not sensitive to culture condi-tions. Grouping the bacteria into strains,however, was shown to provide sensi-tivity to specific culture conditions. Asimilar study was performed by Harz etal.,90 who investigated various cultureconditions on different Staphylococcusstrains and compared them with strainsunder constant growth conditions. Thisstudy showed that strains under differentgrowth conditions could be readilydistinguished with 94.1% accuracy.Fourier transform Raman spectroscopywas separately used to differentiate sixdifferent microorganisms growing onwhole apples, and to differentiate be-tween pathogenic strains and nonpatho-genic strains.91 Xie et al.92 used LTRSto identify single bacterial cells in anaqueous solution and discriminate be-tween six species, based on PCA.Krause et al. combined Raman spectros-copy with fluorescence microscopy andwere able to show that staining ofbacterial cells with fluorophores doesnot affect the acquisition of Ramanspectra from the same cells93 and alsoallows one to easily locate and identifybacteria.94 Harz et al.95 used Ramanspectroscopy as a sensitive assay toanalyze bacteria in the cerebrospinalfluid of patients with bacterial meningi-tis, concluding that the cerebrospinalfluid does not mask the Raman spectrumof a bacterial cell, and that Raman

spectroscopy could be applicable toclinical use in assessing meningitis andafter its treatment. Tripathi et al.96

determined some key experimental pa-rameters for bacterial imaging by Ramanspectroscopy, such as determining thelaser-induced photodamage threshold,the composition of the water matrix, aswell as assessing organism aging inwater. Schmid et al.97 analyzed a dataset consisting of 3642 spectra from 29different strains of bacteria and utilizedGaussian mixture discriminant analysisfor their classification, achieving anaverage specificity of 86.6%. Analysisby support vector machines, on the otherhand, showed an average specificity of87.3%. These examples should demon-strate that single-cell Raman spectrosco-py has proven a viable tool in theanalysis of bacteria, their response todifferent environmental conditions, andtheir identification in complex mixtures.

CONCLUSION

In this article, we reviewed some ofthe most recent and most significantdevelopments in the analysis of singlecells by spontaneous Raman spectrosco-py. The fact that molecularly specificinformation can be gained from singleliving cells, without the need for exog-enous stains, opens up exciting newprospects for real-time and long-termbiochemical research at the level ofsingle cells in vitro and in vivo. Thephysiological state of individual cellscan be assessed, e.g., by characterizingthe rate at which they process metabo-lites or by characterizing translationalactivity of RNA within the cell andspecific cellular compartments. Thecontinuous development of optical tech-nology such as more sensitive red-enhanced, faster detectors; more com-pact and affordable, tunable fiber-lasersources; and robust and compact spec-trometers will lower the cost andcomplexity of these instruments, whileenhancing their utility, such that theywill see widespread use in biomedicalresearch in the near future. Already,pioneer studies have demonstrated thepotential of Raman spectroscopy for invivo cancer demarcation that can guidesurgical procedures or for cell sortingwithout contaminating antibodies orfluorophores.78 We believe that combi-


nations of current technology, e.g.,microfluidic sample delivery, liquidchromatography, or super-resolved opti-cal approaches will soon be combinedwith Raman spectroscopy to enable evenfaster sample analysis or the localizationof chemical products at subwavelength-length scales. Raman spectroscopy hasevolved from an exotic technique thatonly a handful of select research groupswere able to perform to an entireresearch area by itself, enriching andcomplementing other analytical ap-proaches. Such multimodal imagingand analysis tools are currently in theearly phases of making their way intothe market place, but based on everincreasing demands for minimally inva-sive experiments and diagnostics in thelife sciences, laser Raman microspec-troscopy is one of the most powerfultools currently available to label-freeanalyze and characterize living cells andtheir components.

ACKNOWLEDGMENTS

We thank our colleagues at the University ofCalifornia–Davis, specifically, at the NationalScience Foundation (NSF) Center for Biophoton-ics Science and Technology and the University ofBielefeld for their help and contributions duringthe last several years. In particular, we thank Drs.James Chan, Deborah Lieu, Sebastian Wachs-mann-Hogiu, Stephen Lane, Dennis Matthews, andRod Balhorn. We are grateful for the continuedsupport of this work by the NSF Center forBiophotonics Science and Technology, an NSFScience and Technology Center, which is managedby the University of California–Davis, undercooperative agreement no. PHY 0120999 withthe NSF.

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Methods and Applications of Raman … spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biolog-ical cells