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1 July 2008 Measurement of muscle disease by quantitative second-harmonic generation imaging
Author Affiliations +
Abstract
Determining the health of muscle cells by in vivo imaging could impact the diagnosis and monitoring of a large number of congenital and acquired muscular or cardiac disorders. However, currently used technologies are hampered by insufficient resolution, lack of specificity, or invasiveness. We have combined intrinsic optical second-harmonic generation from sarcomeric myosin with a novel mathematical treatment of striation pattern analysis, to obtain measures of muscle contractile integrity that correlate strongly with the neuromuscular health of mice suffering from genetic, acquired, and age-related decline in skeletal muscle function. Analysis of biopsies from a pilot group of human volunteers suggests a similar power in quantifying sarcopenic changes in muscle integrity. These results provide the first strong evidence that quantitative image analysis of sarcomere pattern can be correlated with physiological function, and they invite the application of SHG imaging in clinical practice, either in biopsy samples or via microendoscopy.

1.

Introduction

Muscular disorders (MDs) are a large heterogeneous group of diseases characterized by progressive weakening and degeneration of skeletal muscle tissue. Many forms of congenital MDs are now known to arise from identified single-gene defects. Molecular diagnosis can clarify medical decisions as to the course of treatment, long-term prognosis, and genetic counseling. Still other MDs are associated only secondarily with the onset of disease elsewhere in the patient, e.g., cachexia in cancer, atrophy due to injury or neuropathy, or sarcopenia of aging. For both genetic and nongenetic MDs, monitoring the course of disease and judging the success of treatment are most often approached through methods that are either low-resolution or invasive, without assessing the integrity of muscle fibers directly. Much attention is focused on the prospect of monitoring disease through microarray gene-expression profiling. However, a recent effort to differentiate expression patterns of early and advanced human Duchenne Muscular Dystrophy showed a remarkable constancy in expression profiles, even as disease worsened over many years.1 Several different imaging modalities, such as quantitative computed tomography, dual energy X-ray absorptiometry, and magnetic resonance imaging, provide information about the geometry, mass, and tissue composition of bone and muscle.2 These noninvasive techniques are suitable for rough quantitative measurements of tissue degradation/repair. However, low spatial resolution prevents these methods from probing the microscopic features and molecular structure of skeletal muscle.

Sarcomeres of striated muscle produce the force that is ultimately responsible for contractile function. Expression and catalysis of sarcomeric proteins are well documented to respond to neuromuscular activity or disease,3, 4, 5, 6, 7 making the sarcomere pattern a prime candidate for a visible indicator of myofiber integrity.8, 9, 10 In fact, some published evidence correlates muscular dystrophy with changes in the sarcomere pattern of biopsy samples.8, 11 However, quantitative microscopic assessment of muscle sarcomere damage has never been adopted as a routine diagnostic tool. The absence of appropriately specific, objective, and automated imaging techniques has been the major limitation for the development of such approaches. Traditional histochemical staining to reveal striations (e.g., eosin or Alizarin blue) lacks molecular specificity, as it also labels additional nonsarcomeric intracellular structures and organelles. Laser diffraction has been used to measure a globally averaged sarcomere length in muscle tissue, and has even been applied to live measurement of muscle extension during orthopedic surgery,12, 13, 14, 15 but it does not allow for a micron-by-micron analysis of the contractile lattice within tissue. Observation of sarcomere pattern by either electron, immunofluorescence, or polarization microscopy has very limited applicability in the clinic: none of these imaging techniques can be used to image directly within thick-muscle biopsy samples, and obtaining statistically reliable data requires analysis of hundreds of histological sections. Moreover, no robust means for quantifying the differences between sarcomere patterns in healthy and diseased muscle—to yield objective scores of muscle fiber structure—has been described for any of these techniques.

We present a novel approach to the problem of quantitative diagnostic muscle imaging, by combining a deep-sectioning, high-resolution optical imaging technique with mathematical and statistical analysis of the resulting intrinsic contrast images from muscle tissue. The myosin thick filaments of sarcomeres produce second-harmonic generation (SHG) when excited by a focused ultrafast-pulsed infrared laser, and the synchronized alignment of repeating sarcomeric A-bands in adjacent myofibrils yields a pattern of high-contrast striation within normal muscle fibers that is striking in its regularity of period and orientation. 16, 17, 18, 19, 20, 21, 22 Furthermore, the method yields three-dimensional stacks comprising relatively large volumes of intact tissue, since the deep-sectioning power of SHG imaging allows image acquisition at a submicron resolution up to 600μm into a specimen.

Here, we have assessed the potential of monitoring the progression of muscular disease by quantitative analysis of sarcomere striation patterns in SHG images from muscle samples. By applying the Helmholtz equation for wavenumber to collections of SHG images from groups of individuals, we correlated the distribution of lengths of sarcomeres to the severity of disease, comparing mouse or human muscle suffering atrophic, dystrophic, or sarcopenic decline to matched control specimens. Our data show that quantitative SHG imaging of sarcomeric myosin provides robust discrimination between muscle of healthy animals and those with even mild forms of MDs. Because SHG optics can be adapted by miniaturization for real-time in vivo imaging,23, 24, 25, 26 this method should become valuable for minimally invasive monitoring of muscle structure during medical treatment.

2.

Results

2.1.

Quantification and Classification of Striation Patterns in SHG Images of Muscle

We used the SHG microscope to capture digital image volumes from skeletal muscle tissue from a range of normal and diseased sources. In healthy myofibers, bands of sarcomeric SHG are straight and evenly spaced, with each band lying virtually orthogonal to the long axis (axis of contraction) of the cell. Damaged cells display a range of visible deviations from this norm, sometimes localized to subdomains within a giant myofiber: hypercontraction or hyperextension of striation spacing; misorientation, skewing, or contortion of bands; diminution or complete loss of SHG intensity (Fig. 1 ). An individual region of contractile apparatus can be found to display any one or a combination of these defects. To measure these changes and to assign criteria for scoring normal and abnormal structure, we applied the Helmholtz equation for wavenumber to calculate the local striation spacing and angle of orientation, thus creating new image maps for each of these metrics, in addition to the primary map of SHG intensity [Figs. 2a and 2b ; see Section 4 for details of the algorithm]. The deep optical sectioning power of the SHG microscope produced multislice image stacks that typically comprised more than 5×106μm3 of tissue. Thus, the fine spatial resolution of this sarcomere pattern quantitation (SPQ) algorithm allowed automatic assessment, by three distinct numerical criteria, of each of the tens of thousands of sarcomeres within the group of muscle fibers that were sampled by a single image volume. Unfortunately, the nonlinear dependence of measured SHG intensity upon sample thickness deterred us from assigning reliable abnormal/normal cut-off levels to make diagnostic distinctions based on image brightness. However, by reference to the literature on the physiology and ultrastructure of mammalian sarcomeres, and by empirical fitting to control images, we defined thresholds for scoring of abnormal sarcomere length ( <1.6μm or > 2.8μm , a range characteristic of normal needle biopsy samples27) and striation angle ( <70° or > 110° ). These threshold measurments were independent of specimen-depth effects on image intensity, and they excluded consideration of any region lacking SHG pattern, therefore focusing entirely on myosin-containing structure within myofiber cells. Acquiring these objective SPQ measurements permitted detailed statistical analysis of the effects of specific MDs upon the structure of sarcomeres throughout the imaged volume.

Fig. 1

SHG imaging of normal and diseased muscle. Single optical sections of gastrocnemius muscle dissected from (a) 5-week -old control, (b) mdx;utr++ , and (c) mdx;utr animals, respectively. Yellow arrowheads mark gaps between myofibers; yellow and blue arrows label splits and ruptures, respectively, within the contractile lattice of myofibers; green arrows show myofiber areas with bright unstriated SHG. Scale=20μm . (Color online only.)

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Fig. 2

Sarcomere pattern quantification and correlation of sarcomere-length distribution with severity of muscular disorders. Single SHG optical sections (left panels) and corresponding sarcomere length maps (center panels) and angle maps (right panels) from (a) 5-week -old control and (b) mdx;utr mice. Pseudocolor scale shows sarcomere lengths in μm (center) and angle of striations, relative to the long axis of the muscle fiber, in degrees (right). (c) Sarcomere length distributions affected by both mild and severe forms of muscular dystrophy. Histograms of sarcomere length were assembled from the full collection of SHG image stacks for each condition (see Section 4 for numbers of animals, specimens, and images). Fractions of hypercontracted sarcomeres (<1.6μm) are shown; distributions of non-hypercontracted sarcomeres are filled with color matching the pseudocolor scale shown in (a). (d) Distribution of sarcomere length correlates with severity of various muscle disorders. Two characteristics of the length histograms (FNH and MNHL) were averaged for each entire image stack. Mean and standard deviation were calculated from the full collection of image stacks for each condition (see Section 4 for numbers of animals, specimens, and images). Asterisks (*) indicate data significantly different from control (p<0.05) , as measured by a t -test for independent samples (Statistica 6.0, Statsoft, Inc.), with each image stack treated as an independent sample.

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2.2.

Correlation of Sarcomere Pattern Scores with Damage Due to MDs in Mice

We tested the applicability of the SPQ scoring algorithm to detecting and quantifying disease in muscle. We imaged specimens of gastrocnemius muscle by SHG, comparing SHG image sets from control mice against SHG image data acquired from mice with each of three established models of muscular disorders: disuse-induced atrophy, hereditary muscular dystrophy, and aging (see descriptions of disease models in Section 4). The selected models each incur a characteristic range of damage within the full spectrum of phenotypic severity, allowing us to rate the sensitivity of SHG image analysis in detecting injury. Efforts to combine binary thresholds for both SPQ criteria (length and angle) to measure the abundance of normal versus abnormal sarcomeres provided rather reliable differentiation between healthy and diseased muscle samples (data not shown). However, we found that a detailed analysis focusing specifically on the distribution of sarcomere lengths within specimens produced even better distinctions. A consistent negative correlation between the severity of a disease and the mean sarcomere length was revealed for all disorder models we tested [Fig. 2d]. While sarcomere lengths spanned from 1.4to2.4μm in biopsies of healthy muscles, both mild and severe forms of MDs showed a noticeable reduction in the range of sarcomere lengths and an increased fraction of hypercontracted sarcomeres with lengths below 1.6μm [Figs. 2c and 2d].

To employ these differences in sarcomere length as a diagnostic marker, we extracted three distinct characteristics of the distribution of sarcomere lengths for each sample volume: the overall mean length (ML), the fraction of all sarcomeres that were not hypercontracted below 1.6μm in length (FNH), and the mean length of the fraction of sarcomeres that were not hypercontracted (MNHL). Clear discrimination between healthy and disordered muscle could be achieved using either ML or FNH alone when control mice were compared to mdxutr double mutants, which lack functional dystrophin and utrophin proteins and develop a severe form of Duchenne-like dystrophy [Fig. 2d].28, 29 Similarly, ML showed a significant difference between controls and animals subjected to atrophy by hindlimb suspension (HLS) [Fig. 2d], a protocol that decreased femur bone mineral density by an average of 12% and muscle fiber size by 13% (see Fig. 3 ). Consistent, but not statistically significant, alteration of sarcomere pattern relative to controls was detected in biopsies affected by other milder muscular disorders (mice with mdx single mutations lacking only dystrophin, wild-type mice recovering from atrophy, or wild-type mice of advanced age). These milder forms of disease also displayed a broader range of FNH scores among the acquired image volumes, suggesting a mixture of normal and abnormal sarcomere pattern throughout the tissue of these animals.

Fig. 3

Muscle fiber cross-sectional areas from control, atrophied, and recovering mice. Data were gathered by transverse reslicing of muscle fibers within digital SHG image volumes of muscle. Each myofiber was outlined and the area in pixels was measured in ImageJ. Plot shows average and standard deviation for area measurements from myofibers in both gastrocnemius muscles of all mice included in each group (7 control, 6 HLS, 5 HLS+reloaded ). n=884 measured fibers for control, n=686 for HLS, n=472 for HLS+reloaded .

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To more carefully test the diagnostic efficacy of SPQ scores, we performed receiver operating characteristic (ROC) analysis—both nonparametric and using multivariate logistic regression—to assess the specificity and sensitivity of SPQ measures in distinguishing groups by a clinically relevant statistical test (Table 1 ).30, 31, 32 As expected, evaluation of muscle damage based on a single sarcomere pattern parameter (FNH or MNHL) was sufficient to reliably detect severe MDs, such as dystrophy in mdxutr mutants and muscle atrophy in hindlimb-suspended mice [ROC areas under the curve (AUC) 0.89 , with 95% confidence > 0.790 for both FNH and MNHL in both comparisons; Table 1], at late stages when other signs of disease were clearly observed. Yet the diagnostic power of either of these parameters alone was not sufficient for clear discrimination of any moderate or mild form of MD from control samples (ROC AUC 0.755 , 95% confidence 0.620 for all cases; Table 1).

Table 1

Discrimination of control and diseased mouse muscle by SPQ measurements. Receiver operating characteristic area-under-the-curve (ROC AUC) values are shown in bold face for comparisons among control, mild, and severe disorders. Confidence intervals for each of these calculated values were derived by 1000-fold bootstrap resampling with replacement. The 95% confidence interval for the ROC AUC of each comparison is shown in parentheses under the measured ROC AUC value.

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ML=mean sarcomere length: FNH=fraction of all sarcomeres that were not hypercontracted; MNHL=mean length of no-hypercontracted sarcomeres; BVLR=bivariate logistic regression; TVLR=trivariate logistic regression. Values highlighted in gray have lower limits for 95% confidence which are above 0.74, indicating a good diagnostic test. Numbers of animals, muscle samples, and image stacks analyzed for each group are given in the subsection of Section 4 specific to each mouse model.

Remarkably, however, bivariate logistic regression combining both FNH and MNHL substantially improved the impact of SPQ scoring and allowed us in several cases to efficiently discriminate intermediate forms of MDs from either control samples or severely affected individuals. For example, in a test for the subtle changes brought on by aging, we achieved quite effective differentiation of 24-month -old mouse muscle from 10-month -old specimens (ROC AUC 0.866, 95% confidence 0.786; Table 1). This contrast was detected despite our finding of no significant difference in either agility or the percentage of fat-free body mass between breed-matched animals of these approximate age groups (Fig. 4 ) and the absence of any visibly recognizable alterations of sarcomere pattern. Even more strikingly, both mdx (mild) and mdxutr (severe) dystrophic animals were absolutely discriminated from wild-type controls by a logistic regression model combining all three measures of sarcomere length distribution (ROC AUC 1.000, 95% confidence 1.000; Table 1). The definitiveness of this result stands out, especially with regard to mdx , because the pathology of mdx mice is known to be subtle in the early weeks of life.29, 33, 34, 35

Fig. 4

Mobility performance and body composition of mature adult versus old mice. (a) In agility testing on the rotarod apparatus, each animal was subjected to a warm-up run followed by five sequential mobility tests as described in Section 4. Each of the five bars (left to right) represents the mean value ± SEM of the rotational velocity at which mice fell during each of the five trials: trial 1 (024rpm) , trial 2 (036rpm) , trial 3 (036rpm) , trial 4 (1248rpm) , trial 5 (1248rpm) . n=5 for 10-month -old group, n=10 for 21-month -old group. (b) Body composition measurements from X-ray absorptiometry analysis. n=10 and 20 for mature and aged animals, respectively; all were male C57BL6 mice purchased from NIA-monitored aging colonies. Results were analyzed using one-way ANOVA and, when significant, the Holm–Sidak procedure for pairwise multiple comparisons was performed. (&) represents statistically significant differences (p<0.05) when compared to values in 10-month -old animals.

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To examine the applicability of SPQ scoring in monitoring muscle healing after damage, we analyzed the alteration of sarcomere pattern associated with reloading of HLS-atrophied muscles. Two days of recovery did not change the cross-sectional diameter of muscle fibers in the gastocnemius (Fig. 3), and has been shown in the literature to yield no improvement in either muscle mass or bone mineral density in adult C57BL6 mice (Ref. 36 and our own unpublished data). Nonetheless, we found a clear improvement of muscle SPQ values within hindlimb-reloaded muscle tissue imaged by SHG. In fact, recuperation of pattern within just two days of reloading was sufficient to reliably segregate recovering animals from the fully atrophic (ROC AUC 0.866, 95% confidence 0.799 in the trivariate regression model), and recovering limbs were actually more accurately discerned from HLS-atrophy specimens than from controls (Table 1). This result is remarkable, for it reveals both the speed of contractile adaptation to newly regained mobility and the sensitivity of SHG/SPQ to the early onset of musculoskeletal remodeling during physiological recovery from disease.

2.3.

SPQ of Changes in Human Aging

To consider whether these methods were also applicable in human muscle, and to ask if SPQ would correlate with functional performance, we recruited groups of unrelated young adult and elderly volunteers who underwent tests of physical performance and provided a needle biopsy of the quadriceps muscle, vastus lateralis. We acquired SHG images and calculated SPQ values from each of the biopsies. As was seen for mice, SPQ appeared to efficiently resolve specimens from the young and old groups, even by univariate analysis of individual scores [ROC AUC values FNH=0.866 , MNHL=0.839 , ML=0.797 ; see Fig. 5b ]. Small sample sizes for this study led to decreased lower bounds for confidence in ROC analysis of any of these individual scores. However, we found for human specimens that bivariate and trivariate logistic regression (combining FNH, MNHL, and/or ML) substantially improved the impact of SPQ scoring, to quite closely parallel the results seen for aging in mice [Fig. 5c].

Fig. 5

Quantification of human sarcopenia of aging by SPQ analysis. (a) Means of stack-average values of FNH and ML for biopsies from four young adult (22 image stacks) and 11 old human volunteers (39 image stacks). Error bars show standard deviation. (b) ROC curves for distinction between muscle biopsies from human volunteers. Areas under the curve for individual measures of the sarcomere length distribution were FNH=0.866 , ML=0.797 , MNHL=0.839 (MNHL not shown). (c) Discrimination of control and diseased mouse muscle by SPQ measurements. Receiver operating characteristic area-under-the-curve (ROC AUC) values are shown for comparisons between young healthy adult and elderly volunteers. 95% confidence intervals for the AUC are shown in parentheses. Abbreviations: ML=mean sarcomere length; FNH=fraction of all sarcomeres that were not hypercontracted; MNHL=mean length of non-hypercontracted sarcomeres; BVLR=bivariate logistic regression. TVLR=trivariate logistic regression. Values highlighted in gray have lower limits for 95% confidence which are above 0.74, indicating a good diagnostic test.

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3.

Discussion

Three major conclusions arise from these studies that should impact the future of high-resolution imaging in the assessment of striated muscle disease. First, striation pattern is generally indicative of a broad range of muscle disease. Second, these changes in striation pattern can be quantified automatically to yield statistically powerful measures of muscle health. Third, endogenous SHG offers a means to extract these quantifiable patterns as high-contrast images from muscle tissue in its native state or in vivo.

The periodic structure of contractile machinery lies at the core of muscle function, and this assembly depends upon continual signaling, gene regulation, and biomechanical feedback. We found that three different disorders leading to loss of muscle performance were detectable by SPQ analysis, and quantifiable in their progression, even though none of their etiologies involves a primary molecular defect within the contractile apparatus. Thus, other deficits in muscle homeostasis or function will likely be amenable to SPQ, including neuropathies and cachexia secondary to cancer.3, 37, 38 The SPQ algorithm automatically extracts multiple measured variables from the striation pattern and allows one to transition from raw image to statistical analysis between subjects in a way that can be completely independent of operator judgment or bias. In this study, we analyzed the correlation of a number of SPQ variables (and combinations thereof) with muscle disease. From our current experience, sarcomere length gives the most robust quantification of progression from healthy to mild to severe disease. Why all three disorders studied manifest hypercontraction of sarcomeres is not certain, although dystrophin/utrophin deficiency is known to disrupt myofibril interaction with the cell cortex and to accelerate leakage of calcium into myofibers,39, 40, 41 and it is possible that similar effects are wrought by atrophy and sarcopenia. Nevertheless, this relatively subtle change in banding pattern was the most informative distinction between healthy and compromised muscle, apparently more sensitive than measurements of myofiber or tissue morphology or overt disruptions of the contractile lattice. It is known that muscles change their fiber-type composition in response to some of the disease conditions that we have observed here,42, 43, 44, 45, 46, 47 and such changes might possibly affect our measurements. However, we have examined the sarcomere pattern in muscles with varying proportions of different fiber types, as well as in mice where fiber types are marked by contrasting fluorescent reporter genes, and have not noted any distinctions between the striation patterns of different muscle fiber types (data not shown). We believe, based on these observations, that changes in fiber type probably do not alter the sarcomere length distributions that we have measured. Whatever may be the cause of the sarcomere-shortening effect that we have detected, a relationship between sarcomere length and contraction strength has been well documented,48, 49, 50 suggesting that the loss of performance in muscular disorders may be linked to such structural changes within the cells.

Many mathematical approaches can be applied to texture and pattern analysis in images. The Helmholtz equation works especially well in the context of sarcomeric banding, because the pattern so closely approximates a sine wave in one direction and is uniform in the orthogonal direction. Our analysis method specifically seeks such patterns and identifies the direction and period of the sine wave. In this instance, Helmholtz analysis is more effective than other methods, such as Fourier and wavelet analysis, that decompose the pattern into a sum of waves of predetermined spatial frequencies and orientations, none of which may exactly match the pattern of the image. Already, we have found that Helmholtz approximation yields greater than a 10-fold improvement in pattern-detection sensitivity over an application of 2D wavelet analysis that we have used previously51 (data not shown). Interestingly, efforts at combining measures of sarcomere length with both the angle and SHG intensity of individual striations in a multivariate treatment of the image pattern [an approach designed to capitalize on all of the variables portrayed in Fig. 2a] failed under ROC analysis to equal the power of the approach to SPQ that we have employed here: multivariate logistic regression focused on specific regions of the distribution of sarcomere length alone.

Further improvements upon the performance achieved in this study are still likely, through continued refinement of SPQ algorithms. For example, we have attempted to differentiate muscle biopsies from adult human volunteers diagnosed as normal versus intermediate-frail by Fried’s criteria for frailty.52 Unfortunately, SPQ analyses have not yielded a notable discrimination between two groups of these classes (five and six patients in each group; ROC AUCs: FNH=0.68 , MNHL=0.67 , ML=0.62 ). Similarly, we have not achieved high-quality ROC distinctions between pairs of several of the MD states that we have analyzed: e.g., mdx vs. HLS (ROC AUCs: FNH=0.65 , MNHL=0.66 , ML=0.68 ), or HLS vs. 24-month -old mice (ROC AUCs: FNH=0.73 , MNHL=0.75 , ML=0.79 ). Beyond the elementary level of analysis that we have explored in this paper, more diagnostic potential should result from spatial-distribution analysis of abnormal regions throughout the volume of tissue,53 possibly allowing distinction among different etiologies of disease.

Our assessment of the Helmholtz approach also defines some criteria for the design of imaging systems used for capturing sarcomere pattern. As discussed in Section 4, the image magnification and signal-to-noise ratio in our current images approach the lower limits at which this analysis works efficiently. Reducing image noise could allow for some decrease in magnification, and therefore measurement of more sarcomeres per image. Alternatively, lower noise at the current magnification could allow more detailed analysis of subpopulations of hypercontracted sarcomeres.

While SPQ can be applied to any imaging regime that highlights sarcomere striations, the advantages of SHG contrast make it an especially strong choice for future diagnostic muscle imaging.19 Reports from several labs have used precise morphometric, polarization anisotropy, biochemical, and genetic methods to show that SHG arises from the myosin thick filaments of sarcomeres.16, 18, 19, 20, 21, 22 Intrinsic SHG from myosin harmonophores can be elicited and imaged in living tissue without dyes, and the deep sectioning of SHG imaging yields the equivalent of hundreds of serial histological sections within each spatially unified image volume of tissue. In our work, and in principle within a pathology laboratory, SHG imaging can be done immediately following biopsy collection, and pattern analysis is completed the same day. With a continuation of recent advances in microendoscopy, employing SHG-compatible GRIN lens technology,23, 24, 25, 26 imaging of sarcomere patterns will likely become possible in the tissue of live patients without removing biopsies. Streamlining of automated SPQ algorithms should then allow real-time quantification of myocyte health in the clinic and provide a rapid and unbiased tool for assessing diagnosis and prognosis in a wide range of muscular and myocardial disorders.

4.

Methods

4.1.

Animal Models of Atrophy, Dystrophy, and Sarcopenia

Muscle atrophy was induced in 6-month -old C57BL6 mice by a 14-day course of HLS, as described in the following section. HLS in both mice and rats has been shown to result in substantial atrophy of both muscle and bone within the hind legs during this time span. Returning animals to walking on all fours induces a recovery of muscle force, mass, and fiber size, as well as bone mass recovery, at a rate that is comparable to or slower than the initial rate of atrophy. 42, 43, 44, 54, 55, 56, 57, 58 Mice of the mdx genotype lack functional dystrophin protein and show continual necrosis and regeneration of muscle fibers starting at about 20days of age and extending throughout the full course of life.33, 34, 35, 39 Yet mdx animals show normal muscle strength through much of life and can achieve close to 80% of the life spans of controls.60, 61 In contrast, mdx;utr mice, which lack both dystrophin and the paralogous protein utrophin, display much more rapid incidence of muscle fiber damage and have a maximum lifespan of 20weeks .29 Sarcopenia, the progressive loss of muscle mass and strength with aging, has been found to affect animals across many phyla. To evaluate the effectiveness of SHG imaging in recognizing sarcopenic changes in the mouse, we examined muscle from mice at mid-adulthood ( 10months old) and at an advanced age (24months) close to the average lifespan of the wild type.60 All animal protocols were approved by the Animal Care and Use Committee of the University of Connecticut Health Center (UCHC), Farmington, CT.

4.1.1.

Hindlimb suspension (HLS) protocol

Cages for hindlimb suspension (HLS) of mice were constructed based on the standard HLS cage design for unloading hindlimbs of rats.62, 63 In addition to miniaturizing components, modifications included suspending the mice from a lightweight (26-g) roller ball carriage, comprising two spherical polymer balls rolling on twin rails, that allowed the suspended mice to walk freely around in the rat-sized wire bottom cages.

Six-month-old C57B1/6 females were used. Mice were moved into HLS cages (one mouse per cage) two days before suspension and tails were taped one day before suspension to allow mice to become acclimated to tape. Mice were suspended for 14days . Control mice, and those released from HLS for recovery, resided in normal mouse cages. During this protocol, all mice were housed in a room in which the ambient temperature was maintained at 80°F . Mice had free access to regular food and water. Body weights and behavior were monitored daily during suspension. Mice of three different groups were sacrificed for imaging of muscle from the gastrocnemius of each hind leg: control ( n=7 mice, 14 muscle samples, 119 image stacks analyzed); mice suspended for 14days and sacrificed without return to normal walking (“HLS;” n=6 mice, 12 muscle samples, 89 image stacks analyzed); and mice suspended for 14days and then released for 2days of normal walking (“reload;” n=5 mice, 10 muscle samples, 66 image stacks analyzed).

4.1.2.

Breeding of dystrophic mouse models, mdx and mdx:utr

Founders for the dystrophy model breeding breeding colony were kindly provided by Dr. Melissa Spencer (UCLA). Initial crosses of donated mdx;utr+ females were mated to mdx males ordered from the Jackson Laboratory (Bar Harbor, ME) and rederivation of mdx;utr+ embryos was carried out by staff of the Center for Laboratory Animal Care and the Gene Targeting and Transgenic Facility at UConn Health Center. Genotypes of progeny were determined in each litter by PCR and/or sequencing.28, 29, 59 Five-week-old mice, both male and female, of three different genotypes were sacrificed for imaging of muscle from the gasrocnemius of each hind leg: wild-type C57Bl/6 ( n=6 mice, 12 muscle samples, 128 image stacks analyzed); single-mutant mdx ( n=8 mice, 16 muscle samples, 101 image stacks analyzed); and double-mutant mdx;utr ( n=7 mice, 14 muscle samples, 42 image stacks analyzed).

4.1.3.

Adult and aged mice

For aging studies, 10- and 24month -old C57BL6 males raised at Harlan, Inc. (Indianapolis, IN) were purchased through the National Institute on Aging. The animals were received and housed for acclimatization 57days before each imaging experiment. Animals of two groups were sacrificed for imaging of muscle from the gastrocnemius of each hind leg: 10month -old C57Bl/6 ( n=5 mice, 10 muscle samples, 89 image stacks analyzed); and 24month -old C57Bl/6 ( n=10 mice, 20 muscle samples, 192 image stacks analyzed).

4.1.4.

Mouse muscle samples

Mice were sacrificed by CO2 narcosis, and lower hindlimbs were removed and skinned for dissection. Small specimens of muscle (2mm×1mm×0.5mm) were dissected from the gastrocnemius using the cutting blade of a 4.0-mm OD×80-mm skeletal muscle biopsy needle (Popper & Sons Inc.) and briefly washed in ice-cold phosphate buffered saline (Invitrogen). Adipose and connective tissue were removed by scalpel under a dissecting microscope, and trimmed samples were stored in optical clearing buffer containing 50% glycerol at 20°C until imaging. Optical clearing yields more than a two-fold increase in SHG image contrast for deep optical sections in muscle tissue and substantially diminishes SHG from collagen fibrils within muscle. Previous work has shown virtually no effect of glycerol upon myosin assembly, enzymatic activity, or sarcomere pattern.51, 64, 65, 66, 67

4.1.5.

Human patient biopsy samples

Fifteen patient volunteers were recruited for muscle biopsy. Four young (age 2035years ) and 11 elderly subjects (age 65years and older; average age 79years ) were free of physical disabilities, metastatic cancer, and medication known to affect muscle health. The skin and subcutaneous layer surrounding the biopsy area were anesthetized with 1% lidocaine (Elkins-Sinn, Cherry Hill, NJ), a small incision was made, and a deep-muscle biopsy was taken from the lateral portion of the vastus lateralis ( 20cm above the knee) using a Bergström 5-mm cannula biopsy needle (Depuy Orthopedics, Warsaw, IN) with suction. The tissue sample was immediately trimmed of any visible fat or connective tissue and mounted on the microscope slide in cold sterile saline solution. SHG imaging began within 30minutes of sampling. The numbers of image stacks collected from the young and elderly groups were 22 and 39, respectively. The protocol for this study was approved by the Institutional Review Board of the University of Connecticut Health Center (UCHC), Farmington, CT.

4.1.6.

SHG microscope setup and imaging conditions

All SHG experiments used a nonlinear optical imaging system described previously:18 an Olympus BX61WI upright microscope equipped with a FluoView 300 (Olympus USA) scanning head and Mira 900 Ti-sapphire laser pumped by a 10W , 532-nm Verdi (Coherent). The laser was tuned at 900nm , and average power at the sample plane was adjusted to 1525mW , depending on the sample thickness. A long working-distance 40X, 0.8 N.A. water immersion objective lens and a 0.9 N.A. dry condenser (Olympus USA) were used for excitation and forward-propagating signal collection, respectively. The SHG signal was reflected with a 450-nm hard reflector (TLM2; bandwidth ±45nm ; CVI Laser), isolated from the laser fundamental and any fluorescence by a 450-nm bandpass filter ( 10-nm FWHM, CVI Laser), and detected by a photon-counting photomultiplier module (Hamamatsu 7421, Bridgewater, NJ). Three-dimensional stacks containing up to 150 optical sections at 2-μm increments were acquired at slow scan speed ( 8sper512×512pixel image; 30.5μsperpixel ) with a resolution of 0.29μmperpixel .

4.1.7.

Sarcomere pattern quantification (SPQ)

Computed analysis of sarcomere pattern was performed on SHG image stacks using a custom-designed plug-in collection within the Java-based program ImageJ (http://rsb.info.nih.gov/ij). This MuscleTone package quantifies the tonal component of sarcomere pattern by using the 2D Helmholtz equation to estimate the square of the wavenumber on a pixel-by-pixel basis according to k2=(2p)p , where p is the pixel intensity. The mean and the variance of the estimate for the wavelength (i.e., sarcomere length), λ=2πk , are computed over a small neighborhood of each pixel. If the variance falls below a threshold, then the sarcomere length for that pixel is taken as the mean wavelength. MuscleTone computes 32-bit image maps of the following parameters for the neighborhood of every pixel of the raw image: average pixel intensity, sarcomere length, and striation angle (angle between A-bands and the long axis of the myofiber, assumed to be 90° for the ideal case). The striation angle is estimated by averaging ϕ from tan(ϕ)=(yp)(xp) over the pixel neighborhoods. Images were processed in three spatial frequency bands spanning a range of expected wavelengths between 4 and 12pixels . The radius of the averaging neighborhood for each band was taken as 125% of the upper wavelength limit of that band (up to 15pixels ). The results for the various bands were combined by selecting the band giving the smallest standard deviation of the computed wavelength for each pixel. The maximum standard deviations allowed for assigning a nonzero score were 1.5pixels and 50° for λ and ϕ , respectively. The accuracy of this algorithm and its sensitivity to noise were tested with computer-generated chirped sinusoidal patterns convolved with a level of random noise similar to that in our SHG images, and reliable measurements were obtained for sarcomere lengths of 5to11pixels (1.453.2μm) . This range encompasses the majority of sarcomeres in normal muscle samples.51 Sarcomere length distributions were extracted from the generated maps with the ImageJ histogram tool.

In order to further streamline use of the MuscleTone algorithm on images randomly oriented in the xy -plane, the following method was used to automatically determine the angle perpendicular to the long axis of muscle fibers within the sample. A Gaussian filter with a radius equal to the upper wavelength limit was applied to each slice of the stack. The filtered image was thresholded, using ImageJ’s auto-threshold function to set the limits for the conversion to a binary intensity scale. Finally, the Find Edges filter of ImageJ was applied, and a Radon transform calculation was used to find the angle in the xy -plane with the largest variance along a projection. All sarcomere lengths and striation angles were then determined, as described above, relative to this cross-fiber axis, ϕ0 .

4.2.

ROC Analysis and Bootstrapped Estimation of Error

The receiver operating characteristic curve compares the sensitivity and specificity of a classifier system as the threshold for discrimination is varied, indicating the true-positive and false-positive rates for classification of members of two distinct groups.30, 31, 32 A test with no power in distinguishing between two groups yields a value of 0.5 for the area under the ROC curve (AUC), while a perfect classifier yields 1.0 for the ROC AUC. We calculated ROC curves and AUC values for primary data using STATA and Microsoft Excel. For estimation of error from our relatively small samples (and taking into account dependencies between image volumes from the same animal/patient, leg, or biopsy), we generated 1,000 model data sets for each class of specimen by bootstrap selection of data points with replacement, using custom-written C++ code. Logistic regression models were generated and ROC curve errors were calculated from the bootstrapped data sets using STATA.

4.2.1.

Mouse mobility performance testing

These studies were studies were conducted using a Rotamex 48 RotaRod instrument from Columbus Instruments (Columbus, OH). The testing protocol was designed in order to reliably diagnose the presence of declines in mobility and endurance in mice. Following a brief warm-up session (a linear acceleration from 0to12RPM performed over 5minutes ), each animal underwent five consecutive increasingly challenging trials, each lasting 10minutes . Trial 1 began at 0RPM and accelerated to 24RPM , trials 2–3 began at 0RPM and accelerated to 36RPM , while trials 4–5 began at 12RPM and accelerated to 48RPM . Testing sessions were standardized in terms of time of day, room, and study investigator. Results were analyzed using a repeated-measures ANOVA, allowing for the examination of both between-group effects and within-subject effects. A full model allowed for a simultaneous examination of between-group and within-subject effects.

4.2.2.

Body composition and bone mineral density of mice

Body composition was determined using peripheral dual-energy X-ray absorptiometry (pDXA; PIXImus II; GE-Lunar Corp., Madison, WI). Prior to each series of scans, a tissue calibration scan was performed using the manufacturer’s provided phantom. Mice were anesthetized using 2.5% Isoflurane (IsoFlo; Abbott Laboratories, North Chicago, IL) mixed with oxygen (1.5Lmin) for a period of 810min , including induction and scanning. The mice were then placed in the prone position on a specimen tray and scanned. The head was excluded from total body scans. Information was provided on fat, lean body mass, and bone mineral density involving total body, as well as femoral diaphysis. HLS mice were measured initially after taping the tails, but before suspension, and again at the end of the period of hindlimb suspension.

Acknowledgments

We thank Melissa Spencer for providing founder mice and George Keech and the staff of the Center for Laboratory Animal Care for assistance in establishing the mdxutr mouse colony, and to John Crabbe for advice on rotarod experiments. Sierra Root helped with SHG imaging, Ariel Isaacson and Vaibhav Juneja assisted with work on image-pattern analysis, and Ion Moraru and Jeffrey Dutton helped with data storage and management. This study was supported by grants from the American Heart Association (S.V.P.), the National Science Foundation and Ellison Medical Foundation (W.A.M.), NIBIB (EB001842 to P.J.C. and W.A.M.), NIAMS (AR47673 to C.P.), the TRIHPA endowment and General Clinical Research Center program (MO1-RR06192 to C.J.), the Travelers Chair in Geriatrics and Gerontology and NIA (AR54713 to G.A.K.), and the American Federation for Aging Research (B.Z.).

References

1. 

M. Pescatori, A. Broccolini, C. Minetti, E. Bertini, C. Bruno, A. D’Amico, C. Bernardini, M. Mirabella, G. Silvestri, V. Giglio, A. Modoni, M. Pedemonte, G. Tasca, G. Galluzzi, E. Mercuri, P. A. Tonali, and E. Ricci, “Gene expression profiling in the early phases of DMD: A constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression,” FASEB J., 21 1210 –1226 (2007). 0892-6638 Google Scholar

2. 

V. Preedy and T. Peters, Skeletal Muscle: Pathology, Diagnosis and Management of Disease, , (2002). Google Scholar

3. 

S. H. Lecker, V. Solomon, W. E. Mitch, and A. L. Goldberg, “Muscle protein breakdown and the critical role of the ubiquitin-proteasome pathway in normal and disease states,” J. Nutr., 129 (1), 227s –237s (1999). 0022-3166 Google Scholar

4. 

Y. W. Chen, P. Zhao, R. Borup, and E. P. Hoffman, “Expression profiling in the muscular dystrophies: Identification of novel aspects of molecular pathophysiology,” J. Cell Biol., 151 (6), 1321 –1336 (2000). 0021-9525 Google Scholar

5. 

W. T. Abraham, E. M. Gilbert, B. D. Lowes, W. A. Minobe, P. Larrabee, R. L. Roden, D. Dutcher, J. Sederberg, J. A. Lindenfeld, E. E. Wolfel, S. F. Shakar, D. Ferguson, K. Volkman, J. V. Linseman, R. A. Quaife, A. D. Robertson, and M. R. Bristow, “Coordinate changes in myosin heavy chain isoform gene expression are selectively associated with alterations in dilated cardiomyopathy phenotype,” Mol. Med., 8 (11), 750 –760 (2002). 1076-1551 Google Scholar

6. 

P. G. Giresi, E. J. Stevenson, J. Theilhaber, A. Koncarevic, J. Parkington, R. A. Fielding, and S. C. Kandarian, “Identification of a molecular signature of sarcopenia,” Physiol. Genomics, 21 253 –263 (2005). 1094-8341 Google Scholar

7. 

D. S. Tews and H. H. Goebel, “Diagnostic immunohistochemistry in neuromuscular disorders,” Histopathology, 46 (1), 1 –23 (2005). 0309-0167 Google Scholar

8. 

M. J. Cullen and J. J. Fulthorpe, “Stages in fibre breakdown in Duchenne muscular dystrophy. An electron-microscopic study,” J. Neurol. Sci., 24 (2), 179 –200 (1975). 0022-510X Google Scholar

9. 

S. B. Shah, D. Peters, K. A. Jordan, D. J. Milner, J. Friden, Y. Capetanaki, and R. L. Lieber, “Sarcomere number regulation maintained after immobilization in desmin-null mouse skeletal muscle,” J. Exp. Biol., 204 (10), 1703 –1710 (2001). 0022-0949 Google Scholar

10. 

M. L. Bang, X. Li, R. Littlefield, S. Bremner, A. Thor, K. U. Knowlton, R. L. Lieber, and J. Chen, “Nebulin-deficient mice exhibit shorter thin filament lengths and reduced contractile function in skeletal muscle,” J. Cell Biol., 173 (6), 905 –916 (2006). 0021-9525 Google Scholar

11. 

R. Matsuda, A. Nishikawa, and H. Tanaka, “Visualization of dystrophic muscle fibers in mdx mouse by vital staining with Evans blue: Evidence of apoptosis in dystrophin-deficient muscle,” J. Biochem. (Tokyo), 118 (5), 959 –964 (1995). 0021-924X Google Scholar

12. 

Y. Yeh, R. J. Baskin, R. L. Lieber, and K. P. Roos, “Theory of light diffraction by single skeletal muscle fibers,” Biophys. J., 29 (3), 509 –522 (1980). 0006-3495 Google Scholar

13. 

Y. Lecarpentier, J. L. Martin, V. Claes, J. P. Chambaret, A. Migus, A. Antonetti, and P. Y. Hatt, “Real-time kinetics of sarcomere relaxation by laser diffraction,” Circ. Res., 56 331 –339 (1985). 0009-7330 Google Scholar

14. 

A. Felder, S. R. Ward, and R. L. Lieber, “Sarcomere length measurement permits high resolution normalization of muscle fiber length in architectural studies,” J. Exp. Biol., 208 (16), 3275 –3279 (2005). 0022-0949 Google Scholar

15. 

E. Ponten, S. Gantelius, and R. L. Lieber, “Intraoperative muscle measurements reveal a relationship between contracture formation and muscle remodeling,” Muscle Nerve, 36 (1), 47 –54 (2007). 0148-639X Google Scholar

16. 

P. J. Campagnola, A. C. Millard, M. Terasaki, P. E. Hoppe, C. J. Malone, and W. A. Mohler, “Three-dimensional high-resolution second-harmonic generation imaging of endogenous structural proteins in biological tissues,” Biophys. J., 82 (1), 493 –508 (2002). 0006-3495 Google Scholar

17. 

W. A. Mohler, A. C. Millard, and P. J. Campagnola, “Second harmonic generation imaging of endogenous structural proteins,” Methods, 29 (1), 97 –109 (2003). 1046-2023 Google Scholar

18. 

S. V. Plotnikov, A. C. Millard, P. J. Campagnola, and W. A. Mohler, “Characterization of the myosin-based source for second-harmonic generation from muscle sarcomeres,” Biophys. J., 90 (2), 693 –703 (2006). https://doi.org/10.1529/biophysj.105.071555 0006-3495 Google Scholar

19. 

T. Boulesteix, E. Beaurepaire, M. P. Sauviat, and M. C. Schanne-Klein, “Second-harmonic microscopy of unstained living cardiac myocytes: Measurements of sarcomere length with 20-nm accuracy,” Opt. Lett., 29 (17), 2031 –2033 (2004). https://doi.org/10.1364/OL.29.002031 0146-9592 Google Scholar

20. 

S. Schürmann, C. Weber, R. H. A. Fink, and M. Vogel, “Myosin rods are a source of second harmonic generation signals in skeletal muscle,” Proc. SPIE, 6442 64421U (2007). 0277-786X Google Scholar

21. 

F. Tiaho, G. Recher, and D. Rouede, “Estimation of helical angles of myosin and collagen by second harmonic generation imaging microscopy,” Opt. Express, 15 (19), 12286 –12295 (2007). https://doi.org/10.1364/OE.15.012286 1094-4087 Google Scholar

22. 

C. Greenhalgh, N. Prent, C. Green, R. Cisek, A. Major, B. Stewart, and V. Barzda, “Influence of semicrystalline order on the second-harmonic generation efficiency in the anisotropic bands of myocytes,” Appl. Opt., 46 (10), 1852 –1859 (2007). https://doi.org/10.1364/AO.46.001852 0003-6935 Google Scholar

23. 

E. C. Rothstein, M. Nauman, S. Chesnick, and R. S. Balaban, “Multi-photon excitation microscopy in intact animals,” J. Microsc., 222 (1), 58 –64 (2006). https://doi.org/10.1111/j.1365-2818.2006.01570.x 0022-2720 Google Scholar

24. 

L. Fu, X. Gan, and M. Gu, “Use of a single-mode fiber coupler for second-harmonic-generation microscopy,” Opt. Lett., 30 (4), 385 –387 (2005). https://doi.org/10.1364/OL.30.000385 0146-9592 Google Scholar

25. 

B. A. Flusberg, E. D. Cocker, W. Piyawattanametha, J. C. Jung, E. L. Cheung, and M. J. Schnitzer, “Fiber-optic fluorescence imaging,” Nat. Methods, 2 (12), 941 –950 (2005). 1548-7091 Google Scholar

26. 

M. E. Llewellyn, R. P. J. Barretto, S. L. Delp, and M. J. Schnitzer, “Minimally invasive high-speed imaging of sarcomere contractile dynamics in mice and humans,” Nature (London), 454 (7205), 784 –788 (2008). 0028-0836 Google Scholar

27. 

S. M. Roth, G. F. Martel, and M. A. Rogers, “Muscle biopsy and muscle fiber hypercontraction: A brief review,” Eur. J. Appl. Physiol., 83 (4–5), 239 –245 (2000). 0301-5548 Google Scholar

28. 

R. M. Grady, H. Teng, M. C. Nichol, J. C. Cunningham, R. S. Wilkinson, and J. R. Sanes, “Skeletal and cardiac myopathies in mice lacking utrophin and dystrophin: A model for Duchenne muscular dystrophy,” Cell, 90 (4), 729 –738 (1997). 0092-8674 Google Scholar

29. 

A. E. Deconinck, J. A. Rafael, J. A. Skinner, S. C. Brown, A. C. Potter, L. Metzinger, D. J. Watt, J. G. Dickson, J. M. Tinsley, and K. E. Davies, “Utrophin-dystrophin-deficient mice as a model for Duchenne muscular dystrophy,” Cell, 90 (4), 717 –727 (1997). 0092-8674 Google Scholar

30. 

A. Linden, “Measuring diagnostic and predictive accuracy in disease management: An introduction to receiver operating characteristic (ROC) analysis,” J. Eval Clin. Pract., 12 (2), 132 –139 (2006). 1356-1294 Google Scholar

31. 

M. H. Zweig and G. Campbell, “Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine,” Clin. Chem., 39 (4), 561 –577 (1993). 0009-9147 Google Scholar

32. 

N. A. Obuchowski, “Receiver operating characteristic curves and their use in radiology,” Radiology, 229 (1), 3 –8 (2003). https://doi.org/10.1148/radiol.2291010898 0033-8419 Google Scholar

33. 

G. Bulfield, W. G. Siller, P. A. Wight, and K. J. Moore, “X chromosome-linked muscular dystrophy (mdx) in the mouse,” Proc. Natl. Acad. Sci. U.S.A., 81 (4), 1189 –1192 (1984). 0027-8424 Google Scholar

34. 

Y. Tanabe, K. Esaki, and T. Nomura, “Skeletal muscle pathology in X chromosome-linked muscular dystrophy (mdx) mouse,” Acta Neuropathol. (Berl), 69 (1–2), 91 –95 (1986). 0001-6322 Google Scholar

35. 

C. Pastoret and A. Sebille, “mdx mice show progressive weakness and muscle deterioration with age,” J. Neurol. Sci., 129 (2), 97 –105 (1995). 0022-510X Google Scholar

36. 

M. Roland, A. M. Hanson, C. M. Cannon, L. S. Stodieck, and V. L. Ferguson, “Exercise prevention of unloading-induced bone and muscle loss in adult mice,” Biomed. Sci. Instrum., 41 128 –134 (2005). 0067-8856 Google Scholar

37. 

G. S. Filippatos, S. D. Anker, and D. T. Kremastinos, “Pathophysiology of peripheral muscle wasting in cardiac cachexia,” Curr. Opin. Clini. Nutr. Metab. Care, 249 –254 , (2005). Google Scholar

38. 

J. Machackova, J. Barta, and N. S. Dhalla, “Myofibrillar remodelling in cardiac-hypertrophy, heart failure and cardiomyopathies,” Can. J. Cardiol., 22 (11), 953 –968 (2006). 0828-282X Google Scholar

39. 

P. R. Turner, T. Westwood, C. M. Regen, and R. A. Steinhardt, “Increased protein-degradation results from elevated free calcium levels found in muscle from mdx mice,” Nature (London), 335 (6192), 735 –738 (1988). 0028-0836 Google Scholar

40. 

P. Fong, P. R. Turner, W. F. Denetclaw, and R. A. Steinhardt, “Increased activity of calcium leak channels in myotubes of Duchenne human and mdx mouse origin,” Science, 250 (4981), 673 –676 (1990). 0036-8075 Google Scholar

41. 

J. M. Alderton and R. A. Steinhardt, “Calcium influx through calcium leak channels is responsible for the elevated levels of calcium-dependent proteolysis in dystrophic myotubes,” J. Biol. Chem., 275 (13), 9452 –9460 (2000). 0021-9258 Google Scholar

42. 

D. B. Thomason and F. W. Booth, “Atrophy of the soleus muscle by hindlimb unweighting,” J. Appl. Physiol., 68 (1), 1 –12 (1990). 8750-7587 Google Scholar

43. 

L. Stevens, C. Firinga, B. Gohlsch, B. Bastide, Y. Mounier, and D. Pette, “Effects of unweighting and clenbuterol on myosin light and heavy chains in fast and slow muscles of rat,” Am. J. Physiol.: Cell Physiol., 279 (5), C1558 –1563 (2000). 0363-6143 Google Scholar

44. 

L. Stevens, K. R. Sultan, H. Peuker, B. Gohlsch, Y. Mounier, and D. Pette, “Time-dependent changes in myosin heavy chain mRNA and protein isoforms in unloaded soleus muscle of rat,” Am. J. Phys., 277 (6), C1044 –1049 (1999). 0002-9505 Google Scholar

45. 

V. Siriett, M. S. Salerno, C. Berry, G. Nicholas, R. Bower, R. Kambadur, and M. Sharma, “Antagonism of myostatin enhances muscle regeneration during sarcopenia,” Mol. Ther., 15 (8), 1463 –1470 (2007). Google Scholar

46. 

M. A. Alnaqeeb and G. Goldspink, “Changes in fibre type, number and diameter in developing and ageing skeletal muscle,” J. Anat., 153 31 –45 (1987). 0021-8782 Google Scholar

47. 

J. O. Holloszy, M. Chen, G. D. Cartee, and J. C. Young, “Skeletal muscle atrophy in old rats: Differential changes in the three fiber types,” Mech. Ageing Dev., 60 (2), 199 –213 (1991). 0047-6374 Google Scholar

48. 

V. Joumaa, D. E. Rassier, T. R. Leonard, and W. Herzog, “Passive force enhancement in single myofibrils,” Pfluegers Arch., 455 (2), 367 –371 (2007). https://doi.org/10.1007/s00424-007-0287-2 0031-6768 Google Scholar

49. 

W. Herzog, “Force enhancement and mechanisms of contraction in skeletal muscle,” 2323 –2324 (2005). Google Scholar

50. 

W. Herzog, E. J. Lee, and D. E. Rassier, “Residual force enhancement in skeletal muscle,” J. Physiol. (London), 574 (3), 635 –642 (2006). 0022-3751 Google Scholar

51. 

S. Plotnikov, V. Juneja, A. B. Isaacson, W. A. Mohler, and P. J. Campagnola, “Optical clearing for improved contrast in second harmonic generation imaging of skeletal muscle,” Biophys. J., 90 (1), 328 –339 (2006). https://doi.org/10.1529/biophysj.105.066944 0006-3495 Google Scholar

52. 

L. P. Fried, C. M. Tangen, J. Walston, A. B. Newman, C. Hirsch, J. Gottdiener, T. Seeman, R. Tracy, W. J. Kop, G. Burke, M. A. McBurnie, C. H. S. C. R. Group, “Frailty in older adults: Evidence for a phenotype,” J. Gerontol., Ser. A, 56 (3), M146 –156 (2001). 1079-5006 Google Scholar

53. 

D. Pokrajac, V. Megalooikonomou, A. Lazarevic, D. Kontos, and Z. Obradovic, “Applying spatial distribution analysis techniques to classification of 3D medical images,” Artif. Intell. Med., 33 (3), 261 –280 (2005). 0933-3657 Google Scholar

54. 

M. R. Allen, H. A. Hogan, and S. A. Bloomfield, “Differential bone and muscle recovery following hindlimb unloading in skeletally mature male rats,” J. Musculoskel. Neuron. Interactions, 6 (3), 217 –225 (2006). Google Scholar

55. 

P. E. Mozdziak, P. M. Pulvermacher, and E. Schultz, “Muscle regeneration during hindlimb unloading results in a reduction in muscle size after reloading,” J. Appl. Physiol., 91 (1), 183 –190 (2001). 8750-7587 Google Scholar

56. 

A. Ishihara, F. Kawano, N. Ishioka, H. Oishi, A. Higashibata, T. Shimazu, and Y. Ohira, “Effects of running exercise during recovery from hindlimb unloading on soleus muscle fibers and their spinal motoneurons in rats,” Neurosci. Res. (N Y), 48 (2), 119 –127 (2004). 0077-7846 Google Scholar

57. 

J. E. Stelzer and J. J. Widrick, “Effect of hindlimb suspension on the functional properties of slow and fast soleus fibers from three strains of mice,” J. Appl. Physiol., 95 (6), 2425 –2433 (2003). 8750-7587 Google Scholar

58. 

C. P. Ingalls, G. L. Warren, and R. B. Armstrong, “Intracellular Ca2+ transients in mouse soleus muscle after hindlimb unloading and reloading,” J. Appl. Physiol., 87 (1), 386 –390 (1999). 8750-7587 Google Scholar

59. 

P. Sicinski, Y. Geng, A. S. Ryder-Cook, E. A. Barnard, M. G. Darlison, and P. J. Barnard, “The molecular basis of muscular dystrophy in the mdx mouse: A point mutation,” Science, 244 (4912), 1578 –1580 (1989). 0036-8075 Google Scholar

60. 

J. S. Chamberlain, J. Metzger, M. Reyes, D. Townsend, and J. A. Faulkner, “Dystrophin-deficient mdx mice display a reduced life span and are susceptible to spontaneous rhabdomyosarcoma,” FASEB J., 21 (9), 2195 –2204 (2007). 0892-6638 Google Scholar

61. 

G. S. Lynch, R. T. Hinkle, J. S. Chamberlain, S. V. Brooks, and J. A. Faulkner, “Force and power output of fast and slow skeletal muscles from mdx mice 628months old,” J. Physiol. (London), 535 (2), 591 –600 (2001). 0022-3751 Google Scholar

62. 

M. G. Evans J, “A metabolic cage for the hindlimb suspended rat,” 1 –42 (1994) Google Scholar

63. 

J. S. Harper, G. M. Mulenburg, J. Evans, M. Navidi, I. Wolinsky, and S. B. Arnaud, “Metabolic cages for a space flight model in the rat,” Lab. Anim. Sci., 44 (6), 645 –647 (1994). 0023-6764 Google Scholar

64. 

H. Finck, H. Holtzer, and J. M. Marshall, “An immunochemical study of the distribution of myosin in glycerol extracted muscle,” J. Biophys. Biochem. Cytol., 2 (4), 175 –178 (1956). 0095-9901 Google Scholar

65. 

L. Lorand and C. Moos, “Studies on the biochemistry of contraction and relaxation in glycerinated muscle; the effects of phosphoenolpyruvate,” Biochim. Biophys. Acta, 24 (3), 461 –474 (1957). 0006-3002 Google Scholar

66. 

N. K. Sarkar, A. G. Szent-Gyorgyi, and L. Varga, “Adenosintriphosphatase activity of the glycerol extracted muscle fibres,” Enzymologia, 14 (4), 267 –271 (1950). 0013-9424 Google Scholar

67. 

W. G. Nayler and N. C. Merrillees, “Some observations on the fine structure and metabolic activity of normal and glycerinated ventricular muscle of toad,” J. Cell Biol., 22 (3), 533 –550 (1964). 0021-9525 Google Scholar
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
Sergey V. Plotnikov, Anne M. Kenny, Stephen J. Walsh, Beata Zubrowski, Cherian Joseph, Victoria L. Scranton, George A. Kuchel, Deborah Dauser, Manshan Xu, Carol C. Pilbeam, Douglas J. Adams, Robert P. Dougherty, Paul J. Campagnola, and William A. Mohler "Measurement of muscle disease by quantitative second-harmonic generation imaging," Journal of Biomedical Optics 13(4), 044018 (1 July 2008). https://doi.org/10.1117/1.2967536
Published: 1 July 2008
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