Methods xxx (2013) xxx–xxx

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Methods

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Imaging horse tendons using multimodal 2-photon microscopy

1046-2023/$ - see front matter � 2013 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.ymeth.2013.07.016

⇑ Corresponding author.E-mail address: Sivaguru@illinois.edu (M. Sivaguru).

1 These authors contributed equally to this work.

Please cite this article in press as: M. Sivaguru et al., Methods (2013), http://dx.doi.org/10.1016/j.ymeth.2013.07.016

Mayandi Sivaguru a,⇑,1, John Paul Eichorst a,1, Sushmitha Durgam b, Glenn A. Fried a, Allison A. Stewart b,Matthew C. Stewart b

a Institute for Genomic Biology, University of Illinois Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL 61801, USAb Veterinary Clinical Medicine, University of Illinois Urbana-Champaign, 2001 South Lincoln Avenue, Urbana, IL 61801, USA

a r t i c l e i n f o a b s t r a c t

Article history:Available online xxxx

Keywords:Horse tendonsSecond-harmonic generation microscopy2-Photon fluorescence microscopyCollagen fiber organizationCell/nuclear morphometric analysis andquantificationFluorescence lifetime imaging microscopy

Injuries and damage to tendons plague both human and equine athletes. At the site of injuries, variouscells congregate to repair and re-structure the collagen. Treatments for collagen injury range from simpleprocedures such as icing and pharmaceutical treatments to more complex surgeries and the implantationof stem cells. Regardless of the treatment, the level of mechanical stimulation incurred by the recoveringtendon is crucial. However, for a given tendon injury, it is not known precisely how much of a load shouldbe applied for an effective recovery. Both too much and too little loading of the tendon could be detri-mental during recovery. A mapping of the complex local environment imparted to any cell present atthe site of a tendon injury may however, convey fundamental insights related to their decision makingas a function of applied load. Therefore, fundamentally knowing how cells translate mechanical cues fromtheir external environment into signals regulating their functions during repair is crucial to more effec-tively treat these types of injuries. In this paper, we studied systems of tendons with a variety of 2-pho-ton-based imaging techniques to examine the local mechanical environment of cells in both normal andinjured tendons. These tendons were chemically treated to instigate various extents of injury and in somecases, were injected with stem cells. The results related by each imaging technique distinguish with highcontrast and resolution multiple morphologies of the cells’ nuclei and the alignment of the collagen dur-ing injury. The incorporation of 2-photon FLIM into this study probed new features in the local environ-ment of the nuclei that were not apparent with steady-state imaging. Overall, this paper focuses on horsetendon injury pattern and analysis with different 2-photon confocal modalities useful for wide variety ofapplication in damaged tissues.

� 2013 Elsevier Inc. All rights reserved.

1. Introduction

Tendonitis (or tendinitis) is a common and major orthopedicproblem among human and equine athletes [1–3]. In these cases,the tendon can lose its structural organization and eventually rup-tures as a result of excessive use. Injuries to the tendon are majorcauses of wastage throughout the equine industry [4,5]. Upon in-jury, the damaged location is invaded by cells such as fibroblasts,macrophages, neutrophils and phagocytes to repair and re-struc-ture the tendon. During this process, various factors such as growthfactors are continually released regulating the cellular processes.Horses recover slowly from types of injuries (often requiringmonths) and the probability of re-injury is high. The repaired tis-sue will never have the same physical characteristic as the originaltendon [6].

To make the recovery from tendon injury more efficient,previously reported treatments for tendon injury have ranged fromsimple icing and bandaging to advanced pharmacological treat-ments and surgical procedures [5,7,8]. In limited cases, the injec-tion of stem cells into damaged tissue has led to improvedrecovery times following injury and a reduction in the occurrenceof re-injury when compared to previous treatments [9–14]. Forexample, a recent study reported that bone marrow derived stemcells (BMSCs) repaired damages within a tendon by increasingthe production of collagen and enhancing the alignment of the col-lagen fibers within the tendon [9,10]. However, the usefulness ofinjected stem cells to promote recoveries from injuries to the ten-don is not optimal in all cases and relies greatly on the source ofthe injected stem cells, the timing of the implantation and thenumber of cells implanted [15].

However, throughout the recovery process, the optimal level ofmechanical stimulation is a well debated issue. Although too muchload applied to the recovering tendon can be harmful, the lack of aload during recovery can also cause significant problems. Areference to a ‘‘fine balance’’ occurs frequently in the literature to

2 M. Sivaguru et al. / Methods xxx (2013) xxx–xxx

indicate the appropriate level of mechanical stimulation to applyto a recovering tendon [6,16]. From these studies though, it is clearthat the production of extra cellular matrix proteins necessary forthe repair are mediated by mechanical stimulation during the ten-don’s recovery. Therefore, studying the cell’s response to themechanical forces exerted by tendons at various extents of damageis necessary to make the recovery from tendon injuries moreeffective.

Cells derive cues regulating their behavior in part, from a vari-ety of mechanical forces and stresses imposed on them both bythe re-organizing fibers and the dynamic forces applied to the ten-don [17–20]. Monitoring proteins and organelles within the cell it-self and not just features in the external environment can relatethe extent of mechanical stresses that a cell is actually experienc-ing. As described previously, morphological changes in the cell’sbody, adhesion sites and the nucleus can be instigated by the appli-cation of certain types of mechanical forces [21–28]. The nucleusitself, is physically coupled to the cell’s periphery via the cytoskel-eton and therefore, receives substantial mechanical inputs fromthe extra-cellular space. As a result, extents of nuclear deformationhave been repeatedly instigated in various cell types by both com-pressive and shear forces [21–23,26,28]. Furthermore, nucleardeformation may also modulate gene expression and nucleartransport [29,30]. However, despite the importance of relatingchanges in cellular behaviors to specific features in the localmechanical environment, very few reports have systematicallyexamined nuclear and molecular features within cells respondingto injured tendons.

Therefore, to examine mechanical stimulation during recoveryfrom tendon injury, we have monitored the tendon’s endogenouscells and labeled stems cells in both damaged and normal equinetendons. Multiple modalities of 2-photon imaging were appliedto interrogate the system including second harmonic generation(SHG), 2-photon fluorescence microscopy (TPFM) and fluorescencelifetime imaging microscopy (FLIM). The reduced scattering andlack of out-of-focus bleaching that are inherent to 2-photon fluo-rescence imaging and the imaging with SHG applied here makethese ideal techniques for imaging thick samples of tendon. Witha set of quantitative tools for image analysis presented here, robustdifferences in the fibrillar organization of the collagen along withprecise morphological changes in the nuclei have been reported.Furthermore, the addition of 2-photon FLIM in this study revealedintricacies in the nuclear environment that would have been nearlyimpossible to distinguish with only steady-state fluorescenceimaging. Although similar techniques have been applied to studyother biological systems [31], the results presented in this paperare the first time that these techniques have related quantitativeinsights about mechanical cell-mediated tendon injury in thehorse.

2. Materials and methods

2.1. Second harmonic generation (SHG)

Second harmonic generation (SHG) is a microscopy based onscattering that is the result of a non-linear medium interactingwith an electric field. In this process, the illumination of the samplecan covert the two incoming photons into a photon of higher en-ergy [32–34]. In certain types of biological samples, this signalfrom SHG can be used to image samples with resolution at the cel-lular level and with imaging depths up to several hundred microns.

SHG selectively highlights features in images according to geo-metric parameters of the objects being studied. In general, mole-cules with no inversion symmetry (having no axes of symmetry)are more suitable for SHG. The second order susceptibility specificto each sample’s geometry also relates how favorable the

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orientation of the incoming electric field and that of the moleculeswithin the structure are to produce SHG in each dimension[35–38]. Measuring the emitted SHG signal through both a pola-rizer placed in the excitation and also in the emission channelshas been shown to be a method to determine the elements of thistensor. When combined with the corresponding measured SHGsignal, the orientation of the polarizers will indicate which axesof the molecules are favorable for SHG. The cylindrically symmetric(but non-centrosymmetric i.e. no inversion symmetry) distributionof dipoles in many of the fibrillar structures in collagen does pro-duce a large second order (non-linear) susceptibility.

The intensity from SHG can be detected in the forward andbackward directions [34,38–41]. This can be understood by consid-ering a single molecule scattering light as a result of the second or-der susceptibility. This molecule will emit an electric field in aradially symmetric manner much like a Hertzian dipole. When aset of these scattering molecules are aligned, the scattering thatcan be detected as SHG is biased primarily in the forward direction.Typically, the emitted SHG signal in the backward direction fromthe multiple scattering molecules will interfere destructively (outof phase) eliminating any measured intensity in that direction.However, if the individual scattering molecules are spaced prop-erly, the emitted SHG signal can interfere constructively (in phase)in the backward direction. Therefore, information describing thespacing of scatterers can be obtained by examining the backwardSHG signal. Backward SHG can also be caused by the trivialre-scattering of light emitted from the forward SHG signal.

Previous studies examining fibers of collagen in tendons havehowever, indicated that it was difficult to distinguish the featuresin the image related by the backward and the forward SHG signals[38]. From these measurements, it was hypothesized that the scat-ter from SHG originates solely from the outer shell of the collagenand not from the internal bulk material. In other words, the colla-gen fiber itself is visualized like a thin hollow tube by SHG. How-ever, other results have demonstrated the intensity of lightscattered by either forward or backward SHG depends on the sizeof the fiber being imaged [38]. Mature fibers that are both longerand thicker can be imaged more effectively by forward SHG. Nas-cent fibers are thinner and therefore have more oscillations perunit area in the spatial domain. In these cases, the spacing condi-tion necessary to generate intensity from backward SHG can bemore easily obtained, leading to more efficient imaging with inten-sity from BSHG.

2.2. Fluorescence lifetime imaging microscopy

The time that a fluorescent molecule spends in the excited stateis referred to as the lifetime. When a fluorescent molecule absorbsa brief pulse of incident light, the excited molecule can return tothe ground state through a set of pathways including fluorescenceemission, non-radiative de-excitation and even inter-system cross-ing to a triplet state [42]. The probability that a specific pathwaywill be used depends on the molecule’s local environment. As such,the measured lifetime can be readily perturbed by factors in the lo-cal environment such as pH, ion concentration and even the pres-ence of metabolites.

Regardless of the molecule’s precise local environment, theprobability that a single excited molecule will leave the excitedstates resembles an exponential. As more pathways for de-excita-tion become available, the excited state will be depleted more rap-idly, shortening the lifetime. Following a short pulse of excitinglight, the number of molecules in the excited state is proportionalto the measured fluorescence intensity with the proportionalityconstant being the rate constant for fluorescence emission.

Re-constructing these exponential decays that are typically onthe order of nanoseconds for most fluorescent probes is difficult

x.doi.org/10.1016/j.ymeth.2013.07.016

M. Sivaguru et al. / Methods xxx (2013) xxx–xxx 3

and prone to uncertainties in the subsequent fitting procedures. Inthis paper, we measured lifetimes in the frequency domain byexciting the samples by light having its intensity repetitively mod-ulated. The harmonic content of the fluorescence emission (re-ferred to as the phase delay ð/FÞ and modulation ratio (M))contain information necessary to extract the lifetime [43,44].

In order to avoid using complex fitting algorithms necessary torelate data measured in the frequency domain to the lifetime of thesample, we have used analysis on the polar plot [45–48]. The polarplot is nothing more than a coordinate transform that moves datacollected in the frequency domain, onto a 2D coordinate plane sothat information about the constituents in a sample can be deter-mined graphically. The coordinate transforms are shown below.

x ¼ M cosð/FÞ ð1Þ

y ¼ M sinð/FÞ ð2Þ

Without even knowing the precise lifetime, a polar coordinate isa unique classifier of the sample based on its lifetime. Changes inlifetime can then be observed simply by monitoring the movementof the polar coordinate on a simple Cartesian plane. In this repre-sentation, the polar coordinates describing samples with only asingle lifetime exist on a semi-circle centered at (0.5, 1) with a ra-dius of 0.5. The polar coordinate of a sample characterized by amulti-component lifetime will reside inside the semi-circle.

If necessary, quantitative information about constituent fluoro-phores such as their lifetimes, can be extracted from the polar plot.In order to do so, the coordinate transforms of the polar plot can bewritten as follows,

x ¼X

i

aiMi cosð/F;iÞ ð3Þ

y ¼X

i

aiMi sinð/F;iÞ ð4Þ

In these equations, the polar coordinate computed from the ðMÞand ð/FÞ are expressed as sums of the polar coordinates of the con-stituent fluorophores, each weighted by its respective contributionto measured average intensity ðaiÞ. Therefore, with simple calcula-tions of vectors, the contribution in intensity from constituent fluo-rophores can be computed. In Eq. (3) and (4), the variables ðMiÞ andð/F;iÞ are the modulation ratio and phase delay of the fluorescentconstituent ðiÞ that would be detected if each constituent couldbe measured independently (by itself).

2.3. Sample preparation

Horse tendon samples (normal and tendonitis-induced) wereremoved from 4–8 Quarter horses (age �4 yrs) for this study. Ultr-asonographic evaluation of the flexor tendon and lameness testswas performed prior to the study. The horse was pre-medicatedwith phenylbutazone (2.2 mg/kg), procaine penicillin G(22,000 IU/kg), and received a tetanus toxoid vaccine before inject-ing collagenase. The superficial digital flexor (SDF) tendon was in-jected with sterile bacterial collagenase at the mid-metacarpalregion, followed by an additional dose of penicillin and phenylbu-tazone. The horse was maintained on strict stall confinement for8 weeks and hand walked twice per day for the subsequent8 weeks (total 16 weeks post-injection). The tendon-derived cellswere harvested as previously described [20]. Cells required 2–3passages to generate greater than 10 � 106 cells for injection.Tendon-derived cells were labeled with CM™ Cell Tracker DiI (LifeTechnologies, Eugene, OR) immediately prior to administration,following the manufacturer’s protocol. The collagenase-inducedlesions were treated with 10 � 106 DiI-labeled tendon-derivedcells 21 days after the injury in a 0.3-mL of sterile saline solution

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and injected into the tendon lesion under ultrasound guidance.Next, the subjects were humanely euthanized and the collage-nase-induced diseased tendon as well as tendons injected withtendon derived stem cells labeled with DiI were collected at 1, 2,4 and 6 weeks (one time point was taken here for comparison ofSHG and DiI signals). Normal tendon sample was also collectedfrom the SDF tendon of the other contra lateral fore-limb. All pro-cedures were approved by the University of Illinois InstitutionalAnimal Care and Use Committee.

For cryostat sectioning, the tendon samples were embedded inoptimum cutting temperature compound and preserved at �80 �C.The samples were brought to �20 �C and 25-lm thick sectionswere cut using a cryostat (Leica CM3050S). The samples were thenthawed, secured between two cover glasses (#1.5) with or withoutthe nuclear counter stains DAPI (Emission wavelength over400 nm) or Propidium Iodide (Emission wavelength over 565 nm)both are from Life Technologies (Eugene, OR) and imaged freshor within 24 hours.

For each sample and condition 4–6 2D images of specificlocations were taken and in representative locations, multiple2D images (12–16 optical planes at 0.5 micron interval)through the Z axis was taken (3D), for better address the nu-clear shape and understand collagen organization (SupplementMovies 1–3). For the morphometric analysis both 2D and 3Dimages of representative samples were analyzed and presentedhere.

2.4. Imaging system

The experimental setup used was a modified Zeiss LSM 710system described previously [49] equipped with a tunable Ti:Sapphire laser source (Fig. 1) that produces 70 fs pulses at a rep-etition rate of 80 MHz. The excitation wavelength used in thisstudy was 780 nm and a quarter waveplate was placed in thepath of the excitation laser used to generate circularly polarizedlight (the fundamental laser is plane polarized) for uniform SHGemission from collagen fibers at all orientations. A galvoscannermoved the beam in a raster pattern. The beam was reflected bya short-pass 760 nm dichroic beam splitter and focused ontothe sample using a 40� water-immersion objective with 1.2 NA.The emitted backward SHG signal was collected by the sameobjective, while the forward signal was collected by a 0.55NA objective. Two filters were used in each geometric imageacquisition (forward and backward): one filter (Semrock FF01–680/SP-25) was used to block the laser wavelengths, and theother (Semrock FF01–390/18–25) was a band-pass filter to trans-mit the SHG signal (390 nm). Photomultiplier tubes (HamamatsuR6357 multi-alkali) were used to record the forward and back-ward SHG images.

While collecting SHG, simultaneously the images for DiI (stemcells), DAPI or PI labeled nuclei fluorescence (TPFM) were takenusing additional detectors in the path of cascadable non-descanneddetectors (NDD) with additional emission filters using the sameexcitation for SHG (780 nm). A 500–550 (green) or 565–615(orange) emission band pass filters were used after the backwardSHG detection path using a 400 LP dichroic mirror. These channelsused to collect the DAPI or Propidium Iodide nuclear 2 photon fluo-rescence signals. The average power was 3 mW. Parameters suchas detector gain, pixel dwell time, and frame averaging were main-tained constant between both the forward and backwarddirections.

The Zeiss LSM 710 microscope was modified for lifetime imag-ing by installing an external photomultiplier tube model GaAsPH7422 (Hamamatsu, Japan) and custom lens assembly immedi-ately next to the housing for the non-de-scanned detectors. A Fast-FLIM acquisition unit (ISS Inc., Champaign, IL) processed the signal

x.doi.org/10.1016/j.ymeth.2013.07.016

Fig. 1. Experimental set up used in this study to concurrently image collagen fibers using second harmonic generation (SHG) microscopy and injected stem cells with two-photon fluorescence microscopy (TPFM):See the methods sections for further details. The FLIM module is further down on the NDD light path.

4 M. Sivaguru et al. / Methods xxx (2013) xxx–xxx

from the detectors for calculation of lifetimes in the frequency do-main. The sample was excited with a wavelength of 780 nm usingthe previously described MaiTai 2-photon laser pulsing at 80 MHz.The light emitted from the sample was collected through a 550 nmlong pass filter. The reference standard for the measurements wasRhodamine B in water (lifetime = 1.7 ns [50]).

2.5. Image analysis

2.5.1. Feature extractionThe image containing intensity from both the fluorescence of

DAPI and the backward SHG signal were processed in order to seg-ment the nuclei of the cells. After spatial filtering including bothwavelet-based analysis [51] and median filtering specific to eachimage, intensities were selected in the image using a combinationof basic thresholding and Otsu’s method [52] in MatLab (Natick,MA). The area of all features in the resulting image was calculated.All features in the image with an area smaller than the nuclei(based on visual inspection) were removed. In the final step, dila-tion was applied to match the boundaries of the image more clo-sely to their corresponding features in the image. For each objectthat remained, the area, eccentricity and perimeter of the objectwas calculated in the software MatLab (Natick, MA).

2.5.2. Fourier transformation analysis of collagen and interference ofnuclear frequencies

Every piece of an image can be systematically analyzed accord-ing to the constituent types of repetitions throughout the image. Inmathematical terms, every portion of the image can be expressedas a sum of sine and cosine functions of varying frequency usingFourier analysis [53,54]. The amplitude (or equivalently, contribu-tion) of a sine and cosine function at a specific repetition frequencydepends on the type of feature in the image. In the areas with lotsof thin collagen fibers, the high frequency terms will dominatewhile in regions occupied by larger features such as the nuclei,the low frequency terms will be the more prevalent. The sum ofsine and cosine terms and their corresponding amplitudes derived

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from a specific part of an image are referred to as the frequencyresponse.

In order to interpret and study spatial frequencies in images, theamplitudes of each sine and cosine are squared and presented as afunction of frequency in a representation called a power spectrum.The intensity at the center of the power spectrum relates the aver-age intensity of the image. The location of the points in this imageindicates the frequency of the corresponding parts of the image.The power spectrum relates the abundance of the low frequencyconstituents in the image near the center of the distribution. Thepoints at various distances from the center of the power spectrumindicate that the presence of higher spatial frequencies in theimage.

In this paper, the presented images are Fourier transformed andthe resulting power spectra are thresholded independently (134for the normal tendons and 148 for diseased tendons) in imageJ(http://rsbweb.nih.gov/ij/) to highlight the frequencies abovenoise. Images from MatLab (Natick, MA) outputs are displayed instandard RGB mapping. The raw confocal images (entire panels)are adjusted for brightness if necessary for optimum display of pix-els to represent the image information.

2.5.3. Rendering images on the polar plotA custom software written in MatLab (Natick, MA), rendered

the images shown in Fig. 8 and removed un-modulated back-ground with a blank capture taken during the experiment. Follow-ing the removal of un-modulated background, a 4 � 4 median filterand a threshold of five counts was applied to the images shown inFig. 8.

3. Results and discussion

3.1. Polarization bias with SHG

The amount of light scattered from SHG significantly dependson the polarization of the incident light. Each discrete polarizationof light can only instigate second order scattering from objectsaligned parallel to the incident polarization. In the following

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example demonstrating potential artifacts from a polarization bias,images were collected with SHG from grains of potato starch withdifferent polarizations of the incident light. The large second ordersusceptibility of the starch grains that makes imaging with SHGpossible, arises from the crystalline amylopectin molecules com-prising the individual grains. Likewise, the circular symmetry ofthe starch grain made them ideal to demonstrate these direction-ally dependent biases when imaging with SHG.

The image shown in Fig. 2A was generated by SHG instigated byincident light polarized along the horizontal axis (with respect tothe frame of the image). Throughout each of the round grains de-picted in the image, there is a definite area in the center (red arrowin Fig. 2A, for example) that contains significantly less intensitythan the edges along the grains’ horizontal axes. The size of thisdark area relative to the size of each grain varies depending onthe geometry of the grain. For example, features in the centers ofcertain grains can be resolved while in other cases, the center ofthe grain contains no intensity.

Practically, the polarization of the incident light can be circu-larly polarized by a quarter-wave plate in the excitation path. Inthis approach, much of the incident polarizations can be sampledsimultaneously to generate a more comprehensive image of anobject with SHG. In Fig. 2B, the circularly polarized incident lightwas able to cleanly resolve the entire edge of each grain. In addi-tion, the images of several grains appeared in the top right cornerof the image that were not even apparent in the images collectedusing a single incident polarization of light. Furthermore, theimages of each grain appear fairly symmetric with no obvious biasin illumination along a specific axis. This image could therefore, becalled isotropic or an excitation polarization independent image.

3.2. Tracking cells, nuclei, segmentation and morphometric analysis –endogenous cells

The nuclei of cells stained with either DAPI or PI were imaged inboth damaged and normal tendons. Following our image analysisroutines, the nuclei of each cell was automatically segmentedresulting in the creation of the binary masks shown in Figs. 3Cand 4C. Calculations performed on the masks examined and com-pared the area, perimeter and eccentricity of each nuclei in thesample. In this study, the contribution of autofluorescence fromthe sample was not apparent in the images recorded using back-ward SHG (Fig. 5). For example, the image composed of the tissue’sautofluorescence (Fig. 5D) does not overlay well with the imagegenerated with the intensity from the backward SHG (Fig. 5F).When combined with the loss of intensity from the autofluores-cence spectrum near the spectral range in which the SHG is mon-itored (spectral data in Figs. 5A and B), these results imply that

A B

Fig. 2. Live potato starch granules having amylopectin crystalline molecules which exhiblinearly polarized (polarization angle is shown in the arrow, left to right) before being incfrom the circularly polarized exciting light, highlighted almost all the molecules along t

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there is a negligible level of autofluorescence in our measurementsand SHG signals are pure scatter in the 20 nm bandwidth (380–400 nm).

3.2.1. Single plane image analysisIn Fig. 3, a single plane (median plane) extracted from a z-stack

of images collected with intensities from both backward andforward SHG are shown. In the image of the tendon taken withbackward SHG (green images in Figs. 3A and G), the cells’ nucleistained with DAPI appear as the bright features that are visuallyseparable from the individual collagen fibers. The shape of nucleiin these images ranged from a round morphology (A–F) to aslightly elongated morphology (G–L). The sample described inFigs. 3A–F is a tendon damaged by the collagenase treatment.

The composite image containing intensities from both the back-ward SHG (green) and forward SHG (red) from the damagedtendon sample do not co-localize well particularly along the rightside of the image (Fig. 3A). However in Fig. 3G, the compositeimage from the normal tendon sample shows a high degree ofco-localization between the two images generated with intensitiesfrom the forward and the backward SHG signal respectively. Asdescribed in a previous section, the ratio of images collected withforward and backward SHG can relate information about the spa-tial order of the collagen fibers and also the overall order andlength of the fibers. In this case, the lack of forward SHG signalalong the right side of the image in Fig. 3A, possibly indicates thatthe fibers in that area exist without any preferred orientation.

In this single plane image describing the treated cell, the seg-mentation algorithm clearly distinguished the cell’s nuclei fromthe background of collagen fibers (Figs. 3B and C). The area andperimeter of the nuclei vary in almost a random fashion(Figs. 3D and F). As estimated by the eccentricity (Fig. 3E), theshape of the nuclei also has a wide distribution and is not well de-fined by a discrete value. Furthermore, there is no apparent pat-tern or bias in the position of the nuclei in the image (Fig. 3C).In many cases throughout the image, variations in the density(and therefore, brightness) of the collagen fibers could even bemistaken for nuclei.

In the normal tendon samples, shown in Figs. 3G–L, both thecollagen fibers and the nuclei are well aligned in the vertical direc-tion. In this image, the morphologies of the nuclei are very straightand rod-like paralleling the positions of the collagen fibers(Figs. 3H and I). The eccentricity of nearly all the nuclei exceptthe feature in the top right corner was near unity, indicating theirsimilar elongated shape (Fig. 3K). The spot in the top right cornercould possibly be an artifact arising from the 2-photon laser. How-ever, the area and likewise perimeters of each nucleus still varysignificantly (Figs. 3J and L). When the statistics from both data

it strong second harmonic signals: (A) The exciting light from the 2-photon laser wasident on the imaged sample. (B) In this image, scatter from backward SHG resultinghe entire perimeter of the starch granules.

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A BSHG and DAPI FSHG Composite

+ = B C

Area

800

1200

1600

2000

2400 Area (pixel)

D

0.82

0.86

0.9

0.94 Eccentricity

Eccentricity E

120

160

200

240Perimeter

Perimeter

F

+ =

G BSHG and DAPI FSHG Composite H I

Area J

1000

1200

1400

1600

1800

Area (pixel)

2000 Eccentricity K

0.8

0.85

0.9

0.95 Perimeter L

200

250

300

350 Perimeter

M Treatment (A-F) Control (G-L)

Area 1532.8 ± 565 1281 ± 401

Eccentricity 0.91 ± 0.05 0.96 ± 0.07

Perimeter 179.8 ± 38.1 248.3 ± 80.1

Eccentricity

Boundary From Threshold

Mask

Mask Boundary From Threshold

50 µm

50 µm

Fig. 3. Images showing single plane analysis of nuclear fluorescence (TPFM) and SHG in the damaged (A-F) and normal (G-L) horse tendon: (A) In green, the image of thedamaged tendon generated with the backward SHG signal is contaminated with fluorescence from DAPI (round circles). The image displaying the forward SHG signal (red)obtained in transmission configuration shows no such bleedthrough. (B) The thresholded boundaries computed by the image processing algorithm are projected in white overthe sample’s composite color image. (C) The positions of the nuclei are shown in white in this binary mask. (D-F) Each nucleus in this image is color coded to indicate thecorresponding area, eccentricity and perimeter. The color coded nuclei are shown with a faded version of the composite image from (A). (G) When the normal tendon wasimaged, the images collected using forward SHG (red) co-localized well with most of the features in the green image (backward SHG) as verified by the correspondingcomposite image. (H) The image processing algorithm was able to find the boundaries (plotted as white lines) of the various nuclei. (I) This binary mask highlights the strand-like morphology of several of the nuclei. (J-L) The area, eccentricity and perimeter of the nuclei are presented in color coded images. The position of each nucleus relative tothe original image can be assessed with the faded composite images shown with the color-coded nuclei. (M) The table presented here indicates the mean and standarddeviations calculated for the parameters studied.

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sets are compared (Fig. 3M), the change in shape of the cells ismanifested in the calculated eccentricity.

3.2.2. Maximum intensity projection (3D) image analysisIn a tissue, the cells extend themselves and attach to the tissues

in three dimensions. A single optical section (slice) may likely con-tain small pieces of multiple nuclei. In other words, the true geom-etry of the nuclei can only be assessed by studying all axial planesin which the cells are contained. Several software packages existspecifically to study elements of images in three dimensions. How-ever, even without a complete rendering in three dimensions, amaximum intensity projection can relate many features describing

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objects spanning multiple planes in an image set without extensivecomputations. A maximum intensity projection simply inserts thehighest intensity taken from the entire stack of images for a givenpixel in the frame of the image [55]. In other words, the brightestcomponents from the stack of images are taken on a pixel-by-pixelbasis to form a maximum intensity projection.

In Fig. 4, the maximum intensity projections brought the entirecell population from each data set into view (see Movies 1 and 2 ofeach optical planes of normal and damaged tendons showing nu-clei at different focal planes). In both the damaged and the normaldata sets, the number of cells apparent in the maximum intensityprojection was nearly twice the number of cells contained in the

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BSHG and DAPI FSHG Composite

Area Area (pixel) 1200

2000

2400

1600

Eccentricity

0.7

0.75 0.8

0.85

Eccentricity

0.9

0.95 Perimeter

200

250

300

Perimeter

350

400

Treatment (A-F) Control (G-L) Area 2222.7 ± 485 1576 ± 525.6

Eccentricity 0.93 ± 0.05 0.97 ± 0.09

Perimeter 231.2 ± 38.0 288.0 ± 94.6

+ =

BSHG and DAPI FSHG Composite

Area

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Area (pixel)

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+ = B C A

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Fig. 4. Maximum intensity three dimensional image analysis of nuclear fluorescence (TPFM) and SHG in the damaged (A-F) and normal (G-L) horse tendon: (A). Pseudocolored in green is the maximum intensity image generated with the backward SHG signal while the maximum intensity image collected from the forward SHG signal iscolored in red. (B) The thresholded boundary of each nucleus is plotted in white on this image. (C) This binary mask indicating the locations of the nuclei segmented by theimage processing algorithm shows significantly more nuclei than that which was noted in the 2D single plane analysis (see Fig. 4). (D-F) The area, eccentricity and perimeterof each nucleus are presented as color-coded featuresin each respective image. A faded composite image is placed behind the colored nuclei to provide a context for theirlocation. (G)The maximum intensity projection of the image containing intensities from the backward SHG signal and the fluorescence from the DAPI (green image). Thefeatures in the maximum intensity projection of the forward SHG image (red) do not differ significantly from the corresponding image taken from a single plane. (H) Theboundaries of each of the nuclei are presented in white on top of the composite image. (I) The increase in the number of nuclei in the binary mask relative to that shown in(Fig. 1C) indicates that the nuclei are well spread in the axial direction throughout the sample. (J-L) For each of the nuclei, the area, eccentricity and perimeter were calculated.The results are presented in color-coded images in each of the respective images. (M) In this table, the average area, eccentricity and perimeter is presented along with theircorresponding standard deviations.

M. Sivaguru et al. / Methods xxx (2013) xxx–xxx 7

single (two dimensional) slices examined in the previous section(Figs. 4B, C, H and I). However, the co-localization of the collagenfibers in images derived from the maximum intensity projectionsof the forward and backward SHG signals did not relate any fea-tures that had not been previously observed in the two dimen-sional slices. For example, the dimmer area observed along theright side of the forward SHG image describing the damaged ten-don (Fig. 4A), is still prominent in the maximum intensity projec-tion. Therefore, the lack of long and well-ordered collagen fiberbundles (presence of only young collagen fibers as this might be

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newly formed in response to injury which needs further investiga-tion) in this part of the sample likely persists through multipleplanes in the axial direction.

In the damaged tendon, the segmentation algorithm located nu-clei scattered in a near random manner throughout the frame ofthe image (Figs. 4B and C). The size and shape of the nuclei becamemore consistent throughout this image derived from the maximumintensity projection (Figs. 4J and L). For example, the distributiondescribing the area of each nucleus narrowed while at the sametime, the average area increased relative to the corresponding

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Fig. 5. Spectral imaging to examine autofluorescence: (A) The intensities from each of the regions of interest shown in (B) are plotted as a function of wavelength (starting at419 nm in the x-axis). (B) Three regions of interest (colored boxes) are projected on an image displaying the average intensity at each pixel. The average intensity wascalculated from the stack of spectral images. (C) The image of the sample measured at each emission wavelength is shown. The wavelength corresponding to each image iswritten in white. (D) The average intensity at each pixel computed from the images outlined in blue in (C) is displayed. (E) In this image, the average intensity at each pixelcomputed from the images outline in red in (C) is shown. (F) The image of the collagen was collected with a filter specific for the scatter resulting from BSHG. Please note thatthe features in this image do not co-localize well with the features in the image shown in (D) indicating a lack of autofluorescence in the channel specific for BSHG.

8 M. Sivaguru et al. / Methods xxx (2013) xxx–xxx

values calculated for the single slice. In parallel, the average perim-eter of the nuclei increased also when compared to the calculatedvalues from the single slice. The shape of the nuclei related by thecalculated eccentricities also became much more homogenous.Even without evaluating the statistics, the color-coding in Fig. 4Eclearly shows that majority of nuclei are characterized by aneccentricity near 0.95. In this image unlike the corresponding im-age of the single slice, the orientation of the nuclei clearly followsthe vertical orientation of the collagen fibers as well (the samedirection where the tendon exerts tension in the animal).

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When the maximum intensity projection describing the normaltendon sample was examined, the majority of the nuclei spannedtwo vertical lines along the left side of the image (Figs. 4H and I).With an increase in the number of nuclei in the image, the precisecurvature in the collagen fiber is clearly reflected in the spread ofthe nuclei along these two lines in the left portion of the image.Although the shape of nuclei was well conserved as estimated bythe eccentricities (Fig. 4K), the sizes of the nuclei related by thearea (Fig. 4J) and perimeter (Fig. 4L) parameters still occupied adefinite distribution of values. A visual inspection of the cells along

x.doi.org/10.1016/j.ymeth.2013.07.016

A BSHG BSHG - FFT

C BSHG BSHG - FFT

E BSHG BSHG - FFT

B

D

F

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Fig. 6. Fourier transform (FT-SHG) analysis of collagen organization (backward SHGimages as source): (A)The intensities from the backward SHG signal combined withthe fluorescence of the nuclear stain DAPI are contained in this image of thedamaged horse tendon. (B) The analysis with FT-SHG shows the distribution ofspatial frequencies contained in (A). (C) In the case of normal horse tendons, thisimage shows most of the collagen fibers visualized with the backward SHG signal aswell as the nuclei stained with DAPI are spreading in the vertical direction. (D)Afterdecomposing the image in (C) into frequency space, the calculated contributions ofthe various spatial frequencies imply that the low frequency features dominate thisimage. (E) This image is composed entirely of intensities from backward SHG fromanother normal tendon where the nuclei were counter stained with anotherfluorescent stain PI, which effectively eliminated the bleedthrough encounteredwith DAPI. (F) This power spectrum (content of spatial frequency derived from theFourier transform) corresponding to the image in (E) indicates that low spatialfrequencies are prevalent in the original image. (G) The region highlighted by thewhite dashed line in (A) was cropped and magnified in this representation. (H) Thecropped section of the image still contains predominately low spatial frequencies.

A FSHG FSHG - FFT B

C FSHG FSHG - FFT D

E FSHG FSHG - FFT F

H FSHG - FFT G FSHG

50 µm

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Fig. 7. Fourier transform (FT-SHG) analysis of collagen organization using theforward SHG images: (A) Images of damaged tendon containing intensities onlyfrom the forward SHG signal. (B) This power spectrum relates the content of spatialfrequencies in (A). (C) In the case of normal horse tendons, the image shows thecollagen fibers visualized with only the signal from the forward SHG. (D) This powerspectrum relates the content of spatial frequencies in (C). (E) This image iscomposed entirely of intensities from forward SHG from another normal tendonwhere the nuclei were counter stained with another fluorescent stain PI. (F) Thispower spectrum (content of spatial frequency is derived from the Fouriertransform) corresponding to the image in (E). (G) The region highlighted by thewhite dashed line in (A) was cropped and magnified in this representation. (H) Thedistribution of spatial frequencies derived from the cropped section of the imagecontains both low and high spatial frequencies.

M. Sivaguru et al. / Methods xxx (2013) xxx–xxx 9

the far left column does indicate possible distribution of sizes(areas and perimeters) may be a consequence of the segmentationalgorithm failing to distinguish between the cells and the collagenfibers.

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3.3. Fourier analysis of images collected with forward and backwardSHG – endogenous cells

As discussed previously, images of collagen fibers acquiredusing intensity from both BSHG and FSHG can relate informationabout the length and thickness of the fibers themselves. When cou-pled with quantitative analysis such as Fourier decompositions inthe spatial domain, absolute information about the orientation

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10 M. Sivaguru et al. / Methods xxx (2013) xxx–xxx

and the extent of order of the individual fibers can be obtained[49,56,57].

In Figs. 6 and 7, the images of forward and backward SHGmodalities were decomposed with Fourier analysis to examinetheir content in the space of spatial frequencies. As before, theimages collected with intensities studying the backward SHG sig-nal, contain intensities from the DAPI staining the nuclei of thecells (Figs. 6 and 7A–D). However, the red emission from the nucle-ar stain Propidium Iodide (PI) used instead of DAPI in the samplesdescribed in Figs. 6 and 7E–F, eliminated the unwanted fluores-cence signal in the channel specific for the backward SHG. In eachof the presented data sets, the power spectra derived from the 2DFourier transform taken on each image are presented as binarymasks.

3.3.1. Fourier decomposition of images collected with backward SHGThe images collected with intensities from the backward SHG

signal (Figs. 6A–F) are characterized primarily by features withlow spatial frequencies. The round nuclei stained by the nucleardye DAPI in Fig. 6A contributed for example, to the highest concen-tration of low spatial frequencies near the origin of the image’spower spectrum (Fig. 6B). However, even without the apparent nu-clei such as in the case where the nuclei were stained with PI(Figs. 6E–F), the collagen fibers depicted in the image collected inthe backwards SHG channel did not have the contrast necessaryto create features with high spatial frequencies (Fig. 6F). In otherwords, the additional round nuclei appearing the in the images isnot sufficient to shift the overall composition of the images inspatial frequency collected with the backward SHG signals.

The overall distribution of points in the power spectra shown inFigs. 6B, D and F are spread nearly symmetrically along the hori-zontal axis. Therefore, even in the presence of the low spatialfrequencies, the analysis of the power spectra implies that theaverage orientation of the collagen fibers is in the vertical direction(orthogonal to the horizontal direction). A visual inspection ofthe images does confirm that although the average orientation of

Fig. 8. Tendon studied with FLIM: (A) This steady-state intensity highlights the distributiindicate the positions of the nuclei in the sample. This image was collected with steady-stin (A) and (B) were combined to generate this image which more clearly shows the positi(E) are projected on this polar plot following our image processing routine. The areas thaimage are colored red and green if their corresponding polar coordinates fall into the twthis presentation.

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the collagen is vertical, there are various sub-domains of the colla-gen having orientations several degrees of the left or right of thepurely vertical direction.

3.3.2. Fourier decomposition of images collected with forward SHGThe contrast between individual collagen fibers presented in

the image collected with intensities from the forward SHG channel(Figs. 7A, C and E) is significantly higher than the contrast noted inthe corresponding images collected with the backwards SHGsignal. As a result, the distributions of spatial frequencies derivedfrom these images are populated by much higher frequencies thanthose in the images from the backward SHG channel (Figs. 7B, Dand F). Although the distributions of the spatial frequencies aredistributed in a fairly symmetric fashion along the horizontal axis,the presence of higher frequencies does indicate that multiple dis-crete orientations of the collagen fibers exist, specifically in theimages of the normal tendon shown in Figs. 7C–D. In this distribu-tion of frequencies, two lobes can be seen by drawing one line fromthe lower left corner to the top right corner of the distribution andby drawing another line from the top left corner to the bottomright corner of the same distribution (Fig. 7D). Therefore, thesetwo lobes imply that a portion of the collagen fibers are orientedto left of the vertical while a separate portion of the fibers are ori-ented to the right of the purely vertical direction. By simply lookingat the image in Fig. 7C, the collagen fibers have a repetitive patternsimilar to a ‘‘zig-zag’’ pattern. By comparing the power spectrafrom images collected with the backward SHG signal to the powerspectra indicative of the corresponding forward SHG signal, thefibers appear far more random (non-contiguous fibers due tosustained damage or young newly forming fibers) in the backwardSHG image while the forward SHG image conveys definite informa-tion about the likely preferred orientation of the collagen.

However, the image collected from the forward SHG signal maynot be reliable to distinguish the orientation of the collagen or thetype of collagen fiber present. For example in Fig. 7G, short nascentfibers may appear as black regions in an image collected with

on of collagen in the sample with intensity from backward SHG. (B) The areas in redate 2-photon excitation and a filter specific for the fluorescence of PI. (C) The images

on of the nuclei relative to the collagen fibers. (D) The pixels taken from the image int have a brighter white color indicate a higher density of pixels. (E) The pixels in thiso circles shown in (D). This image was then masked with the DC intensity image for

x.doi.org/10.1016/j.ymeth.2013.07.016

A

B

Fig. 9. Stem cells injected into an injured tendon: (A-B) The images are presented with low (A) and high magnification (B) describe a tendon imaged with backward SHG(green image), forward SHG (blue image) and with 2-photon fluorescence image of the dye Dil (red image). All optical sections can be viewed in the supplemental Movie 1.Scale bar in B represents 10 micrometer.

M. Sivaguru et al. / Methods xxx (2013) xxx–xxx 11

forward SHG. However, in the image containing intensity from thebackward SHG signal (Fig. 6G), an assembly of fibers is clearly vis-ible with an orientation defined by the Fourier analysis (Fig. 6H). Inaddition, the presence of a nuclear dye such as DAPI could contrib-ute intensity in the channel specific for the backward SHG, compli-cating the analysis even further. This examination andclassification of the collagen fiber requires further investigationwith samples differing in age, size, maturity, and also local theenvironment in order to determine which features of the collagenprecisely modulate the intensities recorded with SHG.

3.4. Fluorescence lifetime imaging of the tendon – endogenous cells

The steady-state fluorescence intensities previously collected toimage the stained nuclei of the tendon’s endogenous cells indicateonly the morphology and location of the nuclei. In this case, infor-mation about the cell’s local environment can be potentially de-rived solely from the morphology of the nuclei. Measuredlifetimes of a fluorescent stain can be perturbed by many environ-mental parameters and therefore, can describe environmental het-erogeneities that are not apparent when detecting only steady-state intensities. In this section, lifetime images of the endogenouscells’ stained nuclei were collected from a normal tendon. Intensi-ties from backward SHG were collected in parallel to characterizethe distribution of collagen fibers.

The steady-state images (Figs. 8A–C) convey features previouslydescribed as indicative of normal tendons. The collagen fibers arewell-ordered and adopt a regularly repetitive pattern throughoutthe image (Fig. 8A). The nuclei of these cells are elongated parallelto the stretched collagen fibers (Fig. 8B). When the images of thecollagen and the nuclei are combined, the nuclei themselves fitwell into the empty space between the individual collagen fibers.

Images were collected from the same area described inFigs. 8A–C in the frequency domain with the lifetime imagingequipment. The 550 nm long pass inserted in the emission channelprevented any scatter from SHG from reaching the detector. Fol-lowing the removal of un-modulated background and the applica-tion of a threshold of five counts, the remaining pixels wereprojected as coordinates on the polar plot shown in Fig. 8D. A largedistribution appeared in the lower left corner of the polar plot con-sistent with the previous reports of PI’s lifetime [58,59].

As a result of noise in the detection of light in our system (andin all FLIM systems), the polar coordinates corresponding the even

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a homogenous solution of fluorophore will always appear as asymmetric distribution on the polar plot. As the signal/noise in-creases, the distribution will become smaller. We examined thatdata set describing the PI to determine if there existed multiplediscrete populations (overlapping distributions, for example).Two circular regions of interest shown as the red and green circlesin Fig. 8D were drawn on the polar plot. The pixels correspondingto these polar coordinates were highlighted in the appropriate col-or in the image shown in Fig. 8E. The polar coordinates highlightedin green mainly appear in the bright regions associated with thenuclei while the red regions color the areas between the bright nu-clear features, possibly indicating two populations of nuclear envi-ronments (Fig. 8E). The inset in Fig. 8E demonstrates more clearlythat pixels corresponding to the green regions on the polar plot oc-cupy the bright areas while the dimmer regions nearly the brightcenters correspond to the area highlighted in red on the polar plot.

If there was single distribution on the polar plot, these two re-gions of interest would result in the red and green pixels beingevenly speckled throughout the image in Fig. 8E. Furthermore,the beforehand removal and un-modulated background combinedwith the locations of the two distribution (they do not form astraight with the origin), also implies that residual backgroundmay not have led to the observed phenomena in the images. Inother words, if the dimmer red regions in the image in Fig. 8E sim-ply had more relative background than the brighter green regions,the polar coordinates of the red and green regions would form astraight line emanating from the origin.

The few number of pixels collected in this experiment preventsa detailed fitting to determine the exact nature of the distributions.Much larger sample sizes are necessary to analytically determinethe number and size of constituent distributions of fluorescentcomponents on the polar plot for these types of systems.

3.5. Tracking injected stem cells

In addition to imaging the endogenous cells present in these tis-sues, injected stem cells labelled with the nuclear stain DiL werealso imaged within damaged tendons (red image in Fig. 9A andB). Throughout the images of the collagen collected with SHG,there is an alternating pattern of regions at which there is poorlocalization of the features highlighted by FSHG and those high-lighted by BSHG (alternating dark blue areas in merge image inFig. 9A). The prominence of the intensities from the BSHG in those

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12 M. Sivaguru et al. / Methods xxx (2013) xxx–xxx

areas likely indicates that the collagen fibers are present in shortand tightly packed fibrils. The positions of the DiL labelled nucleiappear to be concentrated in the middle of the image and are notcorrelated well with any specific feature of collagen visualized bySHG.

However, when a small section of the image was analyzed(Fig. 9B), there is a lack of nuclei in the center region of the imagewhere the signal from the BSHG was dominant relative to that ofthe FSHG signal (blue region in merge image in Fig. 9B). The stemcells populate the area surrounding this region with a fairlyhomogenous density (red image in Fig. 9B). The size, morphologyand orientation of the nuclei vary throughout the image. Unlikethe previous examples, it is nearly impossible to even infer the ori-entation of the collagen fibers simply by looking at the distributionof nuclei in the image.

4. Conclusions

With a set of 2-photon imaging techniques, tendons subjectedto different extents of injury were examined from the perspectiveof both the collagen itself and the cells. The nuclear morphology ofthe cells as well as the heterogeneities in the lifetimes within thenuclei likely indicates that both types of cells are experiencing amultitude of features in the local environment.

However, more controls will be needed to relate these types ofmeasurements to precise parameters in the cell’s environment. Inaddition, extending these types of studies into the temporaldimension would likely reveal dynamics in these systems neces-sary to truly debug the processes of differentiation and recoverytaking place in nature.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.ymeth.2013.07.016.

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