Multiphotonic and Harmonic generation microscopy: an attractive label free imaging and non-destructive observation of collagenic and adipose tissues in pathological muscle context

Laurence Dubreil 1,2, Mireille Ledevin 1,2, Claire Lovo 1,2, Thibaut Larcher1,2, Romain Fleurisson 1,2 Lydie Guigand 1,2 and Karl Rouger 1,2 1 INRA UMR703 PAnTher, F-44307 Nantes, France 2 LUNAM Université, Oniris, École nationale vétérinaire, agro-alimentaire et de l’alimentation Nantes-Atlantique, Nantes,

F-44307, France

Second harmonic (SHG) and third harmonic generation (THG) provide powerful tools for collagenic and adipose tissue imaging respectively in unstained section. These properties due to the multiphotonic excitation represent very promising approach to explore the state of various degenerative diseases associated with abnormal fibrotic and fat tissue. In this report, we use SHG/THG to explore the fibrotic and fat tissue in muscular dystrophy context using rat and dog model. By comparison with classical used immunolabeling dedicated to collagen, we confirmed that SHG microscopy has strong potentiality to follow the fibrosis content in the disease. We also demonstrated that THG can be used to follow the distribution of lipid bodies in cells and dystrophic tissue. Thanks to these properties, the combination of fluorescence and harmonic signals represent a promising way to monitor the course of the neuro muscular diseases as well as the efficacy of therapeutic strategies.

Introduction

The theory of the non-linear two-photon absorption process was demonstrated by Göpper-Mayer in 1931 [1] and initially tested by Kaiser in 1961 [2] following the appearance of the first impulse laser sources. These original works showed that the two-photon microscopy is characterized by non destructive and imaging capabilities with a very large spatial resolution in all three dimensions. In addition, two-photon microscopy has a number of unique advantages such as: (i) The increase of penetration depth particularly due to the near-infrared radiation with less absorption in biological specimens than visible light used in confocal microscopy [3, 4]; (ii) The reduction of phototoxicity due to the limitation of the excitation volume at the focal point (sub-femtolitre volume) and therefore the highly compatibility with living biological specimen analysis. First biphotonic microscopic experiments on living cells were performed by Denk et al.,[5] and showed no morphological change of cells containing DNA exposed with two photon near-infrared radiation compare to severe morphological changes observed on cells exposed with the UV radiation. Furthermore, Piston et al., [6] showed that cardiomyocytes labeled with the calcium marker Indo1 for an application of calcium activity imaging survived longer in the case of biphotonic excitation than that generated during single photon excitation; (iii) The increase of the ratio signal/noise. Since the generation of fluorescence occurs only at the focal point, no parasitic signal comes from the planes below and above the focal plane. This property of the microscope thanks to absence of a filter hole allows the detection of the totality of the fluorescence with very little background noise [4]. The biphotonic excitation wavelength is almost twice the fluorescence emission wavelength. It is therefore very easy to separate efficiently the emitted fluorescence signal from the excitation light by using appropriate filters (Fig.1); (iv) The possibility of using simultaneously different modes of contrast in particular of combining multiphoton fluorescence with the Second Harmonic Generation (SHG) and/or Third Harmonic Generation (THG). The SHG is a nonlinear optical process by which two photons of a given frequency are converted into a single photon at double frequency. This phenomenon was observed for the first time by Franken et al., [7] who showed that the spread of a beam from a ruby laser in a quartz crystal generates an optical radiation at the double harmonic frequency. This technique has passed relatively unnoticed for several decades because of the low number of known materials capable of generating this signal. Spatial resolutions of multiphoton excitation fluorescence (2PEF, 3PEF) and harmonic microscopy (SHG, THG) are comparable. However, harmonic generation and multiphotonic microscopy are fundamentally different in nature. Higher harmonic generation (i.e., frequency doubling or tripling) does not arise from absorption but from hyper Rayleigh scattering. SHG and THG imaging are based on the nonlinear scattering of two or three low-energy photons that produce one frequency-doubled or tripled photon, respectively (Fig.1). Multiphoton microscopy is based on the simultaneously absorption on two photons (2P) or three photons (3P) to promote the fluorophore into the excited state and produce emission of one unique photon with a lowest energy than combined energy of exciting photons due to the relaxation of the fluorophore in his excited state (Fig.1).

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

293

___________________________________________________________________________________________

The discovery of endogenous markers that can lead to SHG and to THG has caused interest in biology because makes possible the visualization of molecules or structures without preliminary staining or labeling. SHG allows the observation of organized structures of non centre-symmetric molecules without the contribution of exogenous probes as is often the case in fluorescence microscopy. These dense macrostructures are relatively rare in living tissue which gives the technique a very interesting specificity. The endogenous sources of SHG in biology are mainly fibrillar collagen [8-10], myosin filaments [11-14] and polarized microtubule bundles [15, 16] Myosin filaments and collagen SHG imaging are illustrated in Fig.2A. The SHG was originally applied to collagen in 1979 by Roth and Freund. The molecule of collagen has a characteristic structure in triple right helix of 1.5 nm in diameter and of pitch 8.6 nm, resulting from the association of three alpha chains [17]. It is the triple helix domain that is responsible for the SHG at the molecular level but it is the macromolecular organization that determines the efficiency of SHG. This is why SHG offers excellent specificity for fibrillar collagen especially in the tissue (Fig.2B). SHG microscopy is a powerful modality for imaging fibrillar collagen without exogenous stainings commonly used as the safranin, the trichrome of Masson and the picrosirius or red sirius [18]. These well-known collagen-specific staining methods comprise numerous binding and invasive steps with respect to the tissue in opposition to the free labeled collagen I imaging by SHG microscopy.

As illustrated in Fig.3, collagen was successively stained in red by red picrosirius (Fig. 3A), in green from collagen I immunolabeling (Fig. 3B) and in blue from SHG (Fig. 3C), showing a very similar pattern in serial cardiac muscle sections that confirms the high potentialities of SHG microscopy to explore collagen in muscle without any staining or labeling.

THG is a contrast mechanism that relies on spatial variation intrinsic to the tissue mainly arising from interfaces with lipid-rich molecules. In the THG process, three incident photons are converted into one photon with triple energy and using an excitation wavelength in the 1.2 µm to 1.5 µm excitation range (Fig.1). In contrast to SHG, THG does not required molecular asymmetry. THG is used for imaging optical heterogeneities detected into biological samples to visualize cells, nuclei, vessels, vascularization, as well as morphology of unstained cells and tissues and in particular to detect lipid structures [19-21]. We localized micrometer size lipid in adipocytes in vitro with Nil red fluorescence and blue THG (Fig.4 A-C) as well as collagen SHG in red channel and lipid vesicles THG in blue channel in a rat dystrophic muscle, using respectively multiphoton excitation and harmonic microscopy.

Figure 2: A. blue SHG from myofibrils in mouse cervical muscle, B. blue SHG from collagen in dog skeletal muscle and green dystrophin immunolabeling, excitation wavelength 950 nm (INRA/Oniris UMR703).

Figure 3: Collagen staining in cardiac muscle section. A. red picrosirius staining, B. green fluorescence from collagen I immunolabeling, red fluorescence from bleu evans counterstaining C. blue SHG from collagen, green autofluorescence, excitation wavelength 950 nm (INRA/Oniris UMR703).

Figure 1: Simplified Jablonsky diagram. Emission spectra associated to the excitation 2P/3P. Infra-Red excitation, THG (Third Harmonic Generation), SHG (Second Harmonic Generation) (INRA/Oniris UMR703).

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

294

___________________________________________________________________________________________

Recently studies showed that SHG and THG imaging are promising tools to study label free tissue lesions with potential applications in liver, renal, lung exploration as well as in brain degeneration or human brain tumor imaging [22-25].

Context of the study

Duchenne Muscular Dystrophy (DMD) is a devastating X-linked recessive muscle disease that represents the most common form of muscular dystrophy, affecting one in 3,500 male births [26]. It is caused by mutations or deletions in the gene encoding dystrophin leading to the lack of dystrophin protein. This results in repeated cycles of muscle fiber degeneration followed by muscle fibrosis that is characterized by excessive accumulation of extracellular matrix (ECM) proteins including collagens and fibronectin largely synthesized by fibroblasts in the muscle connective tissue [27, 28]. This leads to a progressive muscle dysfunction and loss of skeletal muscle mass with its substitution by non-contractile fibrotic scar and fat tissues [29]. Clinically, a progressive muscle weakness is observed and premature death occurred at the age of 20-30 years [30]. Then, fibrosis is a prominent pathological feature of muscle tissues from patients suffering from DMD. It is also observed after skeletal muscle denervation and many chronic myopathies [31]. In 2011, we isolated an adult stem cell population we named MuStem cells from healthy dog skeletal muscle based on delayed adhesion properties. We demonstrated that it induces long-term muscle repair and striking clinical efficacy after its systemic delivery in dystrophic dog (named GRMD dog for Golden Retriever Muscular Dystrophy) representing the clinically relevant DMD animal model [32]. To analyze the impact of the MuStem cell transplantation on the muscle tissue remodeling, we determined (i) the regenerative activity by using specific labeling to the developmental MyHC isoform whom expression is restricted to the newly regenerated fibers, (ii) the anysocytosis degree of the muscle fibers by measuring the mean fiber diameter and (iii) the content of ECM by performing collagen I immunolabeling in order to assess the fibrosis in the muscle of transplanted GRMD dogs in comparison to those detected in mock dogs. This last parameter is of major interest for the histopathological description of the course of the disease as well as for the assessment of the investigated treatment [33, 34]. Here, we explored for the first time the potential of SHG imaging to analyze fibrosis in GRMD dog skeletal muscle and proposed a new label free tool to study the muscle tissue remodeling.

Material and Methods

Animals

The animals formed part of a GRMD dog-breeding colony established in France. They were housed and cared for at the Boisbonne Center for Gene and Cell Therapy at Oniris (Nantes, France), according to protocols in compliance with the principles set out in the guidelines of the French National Institute for Agronomic Research (INRA) for the care and use of laboratory animals in biological experimentation. Six 9-month old dogs corresponding to three healthy and three GRMD dogs were included in this study.

Experimental set up

Nikon microscope A1RMP coupled with tunable laser Insight Deepsee 680-1300 nm, pulse width <120 fs from Spectraphysics was used. A scheme of the experimental arrangement is shown in Fig.5. The system was equipped with three high sensitive channels GaAsp Non Descanned Detectors (NDD) and one supplementary channel PMT NDD. Auto laser alignment was performed when changing multiphoton excitation wavelength. The configuration of the filters attached to NDD were (1) band-width 400-492 nm, (2) band-width (500-550 nm), (3) band-width (563-588 nm), (4)

Figure 4: A. Nil red lipid staining, B. blue THG signals from lipid vesicles in adipocytes, C. merge. For THG, excitation wavelength at 1.2 µm, signal detection at 400 nm. D. green collagen SHG and yellow fluorescence of oil red o lipid vesicles stained in rat dystrophic skeletal muscle (excitation at 1040 nm), E red collagen SHG and blue lipid vesicles THG in label free dystrophic skeletal muscle (excitation wavelength at 1.2 µm, signal detection at 400 nm for blue THG and 600 nm for red SHG (INRA/Oniris UMR703).

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

295

___________________________________________________________________________________________

band-width (601-657 nm). The immersion objective used was an apochromat 25X MP1300 objective lens (NA 1.10, WD 2.0 mm). This equipment is available at the APEX platform of the INRA/Oniris UMR 703 unit (Nantes, France). For SHG detection, excitation was performed with the wavelength at 950 nm and signal detection was done at 475 nm. For alexa fluor 488 detection, signal detection was done on green channel with band width 500-550 nm. For 1300 nm excitation wavelength, THG was detected at 433 nm in blue channel and SHG at 650 nm in red channel.

Histochemistry

Immunofluorescent labelings of collagen I were performed on Biceps femoris muscle cryosections of 9-month old dogs. After an incubation overnight at 4°C with antibody directed against collagen I (1/500, MP Bio-medicals, Illkirch, France), section were incubated (1 hour at room temperature) with Alexa fluor 488-conjugated goat anti-mouse IgG (1/300, life Technologies) and mounted in Mowiol.

Histomorphometric analysis

Quantitative analysis was performed using the Fiji open-source platform [35]. Evaluation of the area occupied by SHG signal and/or green fluorescence from collagen I immunolabeling was determined by segmentation analyses. Areas covered respectively by SHG and collagen I immunolabeling were reported to the total area of tissue section analyzed and expressed as a percentage. Colocalized pixel between SHG and collagen I immunolabeling were represented in white color on merge images (Fig. 6). To measure correlation between SHG and fluorescence immunolabeling of collagen I, linear regression analysis was performed using Microsoft excel. R2 and P values were given in legends of Fig.7.

Results and discussion

Same histological pattern was obtained from SHG imaging (blue channel) and fluorescence immunolabeling of collagen I (green channel), in the extracellular matrix of both healthy and GRMD dog skeletal muscles (Fig. 6). White color-pixels in the right panel represented colocalization between blue SHG and green collagen I immunolabeling. SHG/immunolabeling collagen was more intense in GRMD dog skeletal muscle. SHG signals in tissues were related to the high density and quasi-crystalline order in collagen fibrils. Bancelin et al., [36] have shown that the SHG signal varies with the fibril diameter and that there is a minimum size of fibril that can be detected using this technique. This could explain the highly SHG signals observed in the perimysium tissue compare to the endomysium area on skeletal muscle sections. In contrast, specificity of collagen I immunolabeling is related to the accessibility of the epitope recognized by the antibody and to the yield of the in situ immunochemical with a high sensitivity for the collagen fibrils regardless their size.

Figure 5: Set up of the biphotonic microscope Nikon A1RMP coupled with a laser femtosecond Insight Deepsee 680-1300 nm from Spectra-Physics.

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

296

___________________________________________________________________________________________

Table 1: Tissue section area (%) recovered by blue SHG signal and green fluorescence immunolabeling of collagen I. Measurements were performed on three wilde-type dogs and three GRMD dogs. Ten images were analyzed for each dog (750 µm x 750 µm by image).

Dog Healthy1 Healthy2 Healthy3 GRMD1 GRMD2 GRMD3 Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

SHG signal

5.00

2.04

6.00

1.75

3.75

1.54 6.16

1.46

8.91

2.46

10.33

3.77

IHC Col I

5.25

2.13

5.75

2.17

3.83

1.85

8.00

1.75

8.91

2.84

10.75

4.00

Collagen I detected by immunolabeling represented 4.94±0.94% of the muscle section area in healthy dog and 9.22±1.40% of muscle section area in GRMD dog compared to 4.91±1.12% of muscle section area in healthy dog and 8.47±1.15% of muscle section area in GRMD dog when the detection of collagen type I was done from SHG signals (Fig.6). Statistical analyses showed no difference between the two series of measure (student test, p< 0.05). To complete these analyses, values obtained either by immunolabeling or SHG detection were compared for each dog (see table 1 and graph in Fig. 7). These data clearly showed that values obtained from SHG signals were highly correlated with values obtained from collagen I immunolabeling (R2 = 0.9173). SHG signals detected in skeletal muscle can be used for reliable quantification of collagen I detected by immunolabeling commonly used to assess fibrosis in dystrophic skeletal muscle [37].

Figure 6: Collagen imaging in dog skeletal muscles. A. Healthy dog, B. GRMD dog. SHG signal was detected in blue channel. Immunofluorescent labeling of collagen I was detected in green channel from Biceps femoris muscle of 9-month old dogs. Wilde-type and GRMD dog muscles are presented respectively in top and bottom panels. Compared to the control muscle (top panel), GRMD dog muscle displayed a diffuse and irregular thickening of endomysial and perimysial tissue (bottom panel) observed in both blue and green channel. Colocalized pixels between blue and green channel were represented in white colored pixels in right panel.

Figure 7: Numerical values of area (%) recovered by (i) SHG signals and (ii) collagen I immunolabeling (IHC, Col 1 in the table). Analyses were performed with Fiji software. Heathy dogs (blue) and GRMD dogs (red) are represented. R2 = 0.9173. These data clearly show that SHG signals from skeletal muscle can be used for reliable quantification of collagen I.

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

297

___________________________________________________________________________________________

In conclusion, we showed that SHG microscopy is highly specific to fibrillar collagen I in skeletal muscle and we illustrated the sensitivity of this approach in the dystrophic dog model. To validate our SHG experiments, we acquired SHG images from skeletal muscle sections and compared the distribution of the SHG signal with the 2PEF signal acquire from collagen I immunolabeling. Our study showed that SHG signals colocalized with anti-collagen I immunolabeling, in agreement with the literature [38]. SHG microscopy shows many advantages compared to conventional histological technics. This endogenous mode of contrast is applicable to unlabeled sections. Furthermore, SHG microscopy represents an interesting tool to get 3D image reconstruction in comparison to immunohistochemical techniques and histological staining limited to 2D thin sections. Recently, significance of collagen network remodeling was associated with definition of tumor based on the observed changes in collagen organization [23, 39]. These collagen remodeling could provide novel markers to document the disease course at the tissue level and to assess the potential benefit of cell-based therapy strategies. Harmonic generation microscopy represents also a high potential approach to detect THG of lipid bodies in dystrophic muscles in which muscle fibers are gradually replaced by adipose tissue during the course of the disease [40]. Combination of harmonic and fluorescence signals using multiphotonic microscopy gives complementary indicators on fibrosis and adipose infiltration level, making multiphotonic microscopy a promising technique for the tissue phenotyping in animal models with applications in basic research and translational studies, particularly to assess the efficacy of therapeutic strategy dedicated to genetic diseases.

Acknowledgements The support by Pays de la Loire and NeurATRIS is gratefully acknowledged. Images have been performed with an A1RMP biphotonic microscope from the APEX platform UMR703 INRA Oniris, Center of Excellence Nikon Nantes, France.

References [1] M. Göppert-Mayer, “Uber Elementarakte mit zwei Quantensprüngen,” Ann. Phys., vol. 401, no. 3, pp. 273–294, 1931. [2] W. Kaiser and C. Garrett, “Two-Photon Excitation in CaF2:Eu2+,” Phys. Rev. Lett., vol. 7, no. 6, pp. 229–231, 1961. [3] V. E. Centonze and J. G. White, “Multiphoton excitation provides optical sections from deeper within scattering specimens than

confocal imaging.,” Biophys. J., vol. 75, no. 4, pp. 2015–2024, 1998. [4] J. M. Vroom et al., “Depth penetration and detection of pH gradients in biofilms by two- photon excitation microscopy,” Appl.

Environ. Microbiol., vol. 65, no. 8, pp. 3502–3511, 1999. [5] W. Denk, J. H. Strickler, and W. W. Webb, “Two-photon laser scanning fluorescence microscopy.,” Science, vol. 248, no.

4951, pp. 73–6, 1990. [6] D. W. Piston, M. S. Kirby, H. Cheng, W. J. Lederer, and W. W. Webb, “Two-photon-excitation fluorescence imaging of three-

dimensional calcium-ion activity.,” Appl. Opt., vol. 33, no. 4, pp. 662–669, 1994. [7] P. A. Franken, A. E. Hill, C. W. Peters, and G. Weinreich, “Generation of optical harmonics,” Phys. Rev. Lett., vol. 7, no. 4, pp.

118–119, 1961. [8] I. Freund, M. Deutsch, and a Sprecher, “Connective tissue polarity. Optical second-harmonic microscopy, crossed-beam

summation, and small-angle scattering in rat-tail tendon.,” Biophys. J., vol. 50, no. 4, pp. 693–712, 1986. [9] A. Zoumi, X. Lu, G. S. Kassab, and B. J. Tromberg, “Imaging coronary artery microstructure using second-harmonic and two-

photon fluorescence microscopy.,” Biophysical journal, vol. 87, no. 4. pp. 2778–86, 2004. [10] L. W. JIANG et al., “Label-free detection of fibrillar collagen deposition associated with vascular elements in glioblastoma

multiforme by using multiphoton microscopy,” J. Microsc., vol. 0, no. 0, pp. 1–7, 2016. [11] P. J. Campagnola et al., “Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural

proteins in biological tissues.,” Biophys. J., vol. 82, no. 1 Pt 1, pp. 493–508, 2002. [12] D. Roude, G. Recher, J. J. Bellanger, M. T. Lavault, E. Schaub, and F. Tiaho, “Modeling of supramolecular centrosymmetry

effect on sarcomeric SHG intensity pattern of skeletal muscles,” Biophys. J., vol. 101, no. 2, pp. 494–503, 2011. [13] S. H. Huang, C. Der Hsiao, D. S. Lin, C. Y. Chow, C. J. Chang, and I. Liau, “Imaging of zebrafish In Vivo with second-

harmonic generation reveals shortened sarcomeres associated with myopathy induced by statin,” PLoS One, vol. 6, no. 9, 2011. [14] W. P. Dempsey, N. O. Hodas, A. Ponti, and P. Pantazis, “Determination of the source of SHG verniers in zebrafish skeletal

muscle.,” Sci. Rep., vol. 5, p. 18119, 2015. [15] D. a Dombeck, K. a Kasischke, H. D. Vishwasrao, M. Ingelsson, B. T. Hyman, and W. W. Webb, “Uniform polarity

microtubule assemblies imaged in native brain tissue by second-harmonic generation microscopy.,” Proc. Natl. Acad. Sci. U. S. A., vol. 100, no. 12, pp. 7081–7086, 2003.

[16] L. Fritzky and D. Lagunoff, “Advanced methods in fluorescence microscopy.,” Anal. Cell. Pathol. (Amst)., vol. 36, no. 1–2, pp. 5–17, 2013.

[17] K. Beck and B. Brodsky, “Supercoiled protein motifs: the collagen triple-helix and the alpha-helical coiled coil.,” J. Struct. Biol., vol. 122, no. 1–2, pp. 17–29, 1998.

[18] F. Sweat, H. Puchtler, and S. I. Rosenthal, “Sirius Red F3BA as a Stain for Connective Tissue,” Arch. Pathol., vol. 78, pp. 69–72, 1964.

[19] Y. Barad, H. Eisenberg, M. Horowitz, and Y. Silberberg, “Nonlinear scanning laser microscopy by third harmonic generation,” Appl. Phys. Lett., vol. 70, no. 8, p. 922, 1997.

[20] D. Débarre et al., “Imaging lipid bodies in cells and tissues using third-harmonic generation microscopy.,” Nat. Methods, vol. 3, no. 1, pp. 47–53, 2006.

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

298

___________________________________________________________________________________________

[21] H. Lim, D. Sharoukhov, I. Kassim, Y. Zhang, J. L. Salzer, and C. V. Melendez-Vasquez, “Label-free imaging of Schwann cell myelination by third harmonic generation microscopy,” Proc. Natl. Acad. Sci., vol. 111, no. 50, pp. 18025–18030, 2014.

[22] W. L. Chen, P. S. Hu, A. Ghazaryan, S. J. Chen, T. H. Tsai, and C. Y. Dong, “Quantitative analysis of multiphoton excitation autofluorescence and second harmonic generation imaging for medical diagnosis,” Computerized Medical Imaging and Graphics, vol. 36, no. 7. pp. 519–526, 2012.

[23] K. Burke et al., “Using second harmonic generation to predict patient outcome in solid tumors.,” BMC Cancer, vol. 15, no. 1, p. 929, 2015.

[24] N. V. Kuzmin et al., “Third harmonic generation imaging for fast, label-free pathology of human brain tumors,” Biomed. Opt. Express, vol. 7, no. 5, p. 1889, 2016.

[25] S. Xu et al., “Quantification of liver fibrosis via second harmonic imaging of the Glisson’s capsule from liver surface,” J. Biophotonics, vol. 9, no. 4, pp. 351–363, 2016.

[26] A. E. H. Emery, “Population frequencies of inherited neuromuscular diseases-A world survey,” Neuromuscul. Disord., vol. 1, no. 1, pp. 19–29, 1991.

[27] T. A. Wynn, “Common and unique mechanisms regulate fibrosis in various fibroproliferative diseases,” Journal of Clinical Investigation, vol. 117, no. 3. pp. 524–529, 2007.

[28] T. A. Wynn, “Cellular and molecular mechanisms of fibrosis,” J Pathol, vol. 214, no. 2, pp. 199–210, 2008. [29] A. E. H. Emery, “The muscular dystrophies.,” Lancet, vol. 359, no. 9307, pp. 687–95, 2002. [30] A. E. H. Emery, “Duchenne muscular dystrophy-Meryon’s disease,” Neuromuscul. Disord., vol. 3, no. 4, pp. 263–266, 1993. [31] B. Banker and A. Engel, “Basic reactions of muscle. In: Engel AG,” Franzini-Armstrong C Myol. McGraw-Hill, New York, pp.

691–748, 2004. [32] K. Rouger et al., “Systemic delivery of allogenic muscle stem cells induces long-term muscle repair and clinical efficacy in

duchenne muscular dystrophy dogs,” Am. J. Pathol., vol. 179, no. 5, pp. 2501–2518, 2011. [33] F. Robriquet et al., “Differential gene expression profiling of dystrophic dog muscle after MuStem cell transplantation,” PLoS

One, vol. 10, no. 5, 2015. [34] A. Lardenois et al., “Quantitative proteome profiling of dystrophic dog skeletal muscle reveals a stabilized muscular

architecture and protection against oxidative stress after systemic delivery of MuStem cells,” Proteomics, vol. 16, no. 14, pp. 2028–2042, 2016.

[35] J. Schindelin et al., “Fiji: an open-source platform for biological-image analysis,” Nat. Methods, vol. 9, no. 7, pp. 676–82, 2012. [36] S. Bancelin et al., “Determination of collagen fibril size via absolute measurements of second-harmonic generation signals.,”

Nat. Commun., vol. 5, no. SEPTEMBER, p. 4920, 2014. [37] P. Pessina et al., “Novel and optimized strategies for inducing fibrosis in vivo: focus on Duchenne Muscular Dystrophy.,”

Skelet. Muscle, vol. 4, p. 7, 2014. [38] M. Strupler et al., “Second harmonic imaging and scoring of collagen in fibrotic tissues.,” Opt. Express, vol. 15, no. 7, pp.

4054–4065, 2007. [39] M. W. Conklin et al., “Aligned collagen is a prognostic signature for survival in human breast carcinoma,” Am. J. Pathol., vol.

178, no. 3, pp. 1221–1232, 2011. [40] Y. Pereon, S. Mercier, and A. Magot, “[Duchenne muscular dystrophy pathophysiology].,” Arch. Pediatr., vol. 22, no. 12 Suppl

1, p. 12S18-23, 2015.

Microscopy and imaging science: practical approaches to applied research and education (A. Méndez-Vilas, Ed.)

299

___________________________________________________________________________________________