Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle.

IF 5.3 2区 医学 Q2 CELL BIOLOGY Skeletal Muscle Pub Date : 2019-01-08 DOI:10.1186/s13395-018-0186-6
Thibaut Desgeorges, Sophie Liot, Solene Lyon, Jessica Bouvière, Alix Kemmel, Aurélie Trignol, David Rousseau, Bruno Chapuis, Julien Gondin, Rémi Mounier, Bénédicte Chazaud, Gaëtan Juban
{"title":"Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle.","authors":"Thibaut Desgeorges,&nbsp;Sophie Liot,&nbsp;Solene Lyon,&nbsp;Jessica Bouvière,&nbsp;Alix Kemmel,&nbsp;Aurélie Trignol,&nbsp;David Rousseau,&nbsp;Bruno Chapuis,&nbsp;Julien Gondin,&nbsp;Rémi Mounier,&nbsp;Bénédicte Chazaud,&nbsp;Gaëtan Juban","doi":"10.1186/s13395-018-0186-6","DOIUrl":null,"url":null,"abstract":"<p><p>Adult skeletal muscle is capable of complete regeneration after an acute injury. The main parameter studied to assess muscle regeneration efficacy is the cross-sectional area (CSA) of the myofibers as myofiber size correlates with muscle force. CSA analysis can be time-consuming and may trigger variability in the results when performed manually. This is why programs were developed to completely automate the analysis of the CSA, such as SMASH, MyoVision, or MuscleJ softwares. Although these softwares are efficient to measure CSA on normal or hypertrophic/atrophic muscle, they fail to efficiently measure CSA on regenerating muscles. We developed Open-CSAM, an ImageJ macro, to perform a high throughput semi-automated analysis of CSA on skeletal muscle from various experimental conditions. The macro allows the experimenter to adjust the analysis and correct the mistakes done by the automation, which is not possible with fully automated programs. We showed that Open-CSAM was more accurate to measure CSA in regenerating and dystrophic muscles as compared with SMASH, MyoVision, and MuscleJ softwares and that the inter-experimenter variability was negligible. We also showed that, to obtain a representative CSA measurement, it was necessary to analyze the whole muscle section and not randomly selected pictures, a process that was easily and accurately be performed using Open-CSAM. To conclude, we show here an easy and experimenter-controlled tool to measure CSA in muscles from any experimental condition, including regenerating muscle.</p>","PeriodicalId":21747,"journal":{"name":"Skeletal Muscle","volume":"9 1","pages":"2"},"PeriodicalIF":5.3000,"publicationDate":"2019-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13395-018-0186-6","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Skeletal Muscle","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13395-018-0186-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 5

Abstract

Adult skeletal muscle is capable of complete regeneration after an acute injury. The main parameter studied to assess muscle regeneration efficacy is the cross-sectional area (CSA) of the myofibers as myofiber size correlates with muscle force. CSA analysis can be time-consuming and may trigger variability in the results when performed manually. This is why programs were developed to completely automate the analysis of the CSA, such as SMASH, MyoVision, or MuscleJ softwares. Although these softwares are efficient to measure CSA on normal or hypertrophic/atrophic muscle, they fail to efficiently measure CSA on regenerating muscles. We developed Open-CSAM, an ImageJ macro, to perform a high throughput semi-automated analysis of CSA on skeletal muscle from various experimental conditions. The macro allows the experimenter to adjust the analysis and correct the mistakes done by the automation, which is not possible with fully automated programs. We showed that Open-CSAM was more accurate to measure CSA in regenerating and dystrophic muscles as compared with SMASH, MyoVision, and MuscleJ softwares and that the inter-experimenter variability was negligible. We also showed that, to obtain a representative CSA measurement, it was necessary to analyze the whole muscle section and not randomly selected pictures, a process that was easily and accurately be performed using Open-CSAM. To conclude, we show here an easy and experimenter-controlled tool to measure CSA in muscles from any experimental condition, including regenerating muscle.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Open-CSAM,一种半自动分析成人再生骨骼肌肌纤维横截面积的新工具。
成人骨骼肌在急性损伤后能够完全再生。评估肌肉再生效果的主要参数是肌纤维的横截面积(CSA),因为肌纤维大小与肌肉力量相关。CSA分析可能很耗时,并且在手动执行时可能会触发结果的可变性。这就是为什么开发程序来完全自动化CSA分析的原因,例如SMASH, MyoVision或MuscleJ软件。虽然这些软件可以有效测量正常或肥厚/萎缩肌肉的CSA,但它们不能有效测量再生肌肉的CSA。我们开发了Open-CSAM,一个ImageJ宏,在各种实验条件下对骨骼肌上的CSA进行高通量半自动分析。宏允许实验人员调整分析和纠正自动化所做的错误,这是完全自动化程序不可能做到的。我们发现,与SMASH、MyoVision和MuscleJ软件相比,Open-CSAM更准确地测量再生和营养不良肌肉的CSA,并且实验者之间的差异可以忽略不计。我们还表明,为了获得具有代表性的CSA测量,有必要分析整个肌肉切片,而不是随机选择的图片,使用Open-CSAM可以轻松准确地完成这一过程。总之,我们在这里展示了一种简单的实验控制工具,可以在任何实验条件下测量肌肉中的CSA,包括再生肌肉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Skeletal Muscle
Skeletal Muscle CELL BIOLOGY-
CiteScore
9.10
自引率
0.00%
发文量
25
审稿时长
12 weeks
期刊介绍: The only open access journal in its field, Skeletal Muscle publishes novel, cutting-edge research and technological advancements that investigate the molecular mechanisms underlying the biology of skeletal muscle. Reflecting the breadth of research in this area, the journal welcomes manuscripts about the development, metabolism, the regulation of mass and function, aging, degeneration, dystrophy and regeneration of skeletal muscle, with an emphasis on understanding adult skeletal muscle, its maintenance, and its interactions with non-muscle cell types and regulatory modulators. Main areas of interest include: -differentiation of skeletal muscle- atrophy and hypertrophy of skeletal muscle- aging of skeletal muscle- regeneration and degeneration of skeletal muscle- biology of satellite and satellite-like cells- dystrophic degeneration of skeletal muscle- energy and glucose homeostasis in skeletal muscle- non-dystrophic genetic diseases of skeletal muscle, such as Spinal Muscular Atrophy and myopathies- maintenance of neuromuscular junctions- roles of ryanodine receptors and calcium signaling in skeletal muscle- roles of nuclear receptors in skeletal muscle- roles of GPCRs and GPCR signaling in skeletal muscle- other relevant aspects of skeletal muscle biology. In addition, articles on translational clinical studies that address molecular and cellular mechanisms of skeletal muscle will be published. Case reports are also encouraged for submission. Skeletal Muscle reflects the breadth of research on skeletal muscle and bridges gaps between diverse areas of science for example cardiac cell biology and neurobiology, which share common features with respect to cell differentiation, excitatory membranes, cell-cell communication, and maintenance. Suitable articles are model and mechanism-driven, and apply statistical principles where appropriate; purely descriptive studies are of lesser interest.
期刊最新文献
3D-environment and muscle contraction regulate the heterogeneity of myonuclei. Spiny mice are primed but fail to regenerate volumetric skeletal muscle loss injuries. Decreased number of satellite cells-derived myonuclei in both fast- and slow-twitch muscles in HeyL-KO mice during voluntary running exercise. Metabolic pathways for removing reactive aldehydes are diminished in the skeletal muscle during heart failure. Exercise, disease state and sex influence the beneficial effects of Fn14-depletion on survival and muscle pathology in the SOD1G93A amyotrophic lateral sclerosis (ALS) mouse model.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1