In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing
In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing
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DOI：
10.1038/srep13885
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发表时间：
2015-09-18
影响因子：
4.6
通讯作者：
Meissner, Alexander
中科院分区：
文献类型：
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作者：
Aguilar, Carlos A.;Shcherbina, Anna;Meissner, Alexander
创伤性下肢肌肉骨骼损伤在运动员和军人中普遍存在，通常一个人在完全愈合之前返回活动，增加了额外损伤和慢性疼痛的易感性。监测肌肉骨骼损伤后的愈合进展通常涉及不同类型的成像，但这些方法有几个缺点。从受伤部位分离和分析转录本将消除这些缺点，并为损伤后个体肌肉的再生潜力提供枚举性的见解。在这项研究中，使用高通量rna测序（RNA-Seq）对小鼠模型进行创伤性损伤，并在3小时至1个月内检查愈合进展。对全基因组数据集的综合分析显示，受损部位是一个动态的、异质性的环境，由多种细胞类型和数千个在高度调控的网络中经历显著表达变化的基因组成。采用了四种独立的方法来确定损伤后不同时间点最具特征的基因、亚型和遗传途径，并开发了两种新方法来对不同时间点的损伤组织进行分类。这些结果强调了通过使用高通量测序的转录物分析来定量跟踪原位愈合进展的可能性。
Traumatic lower-limb musculoskeletal injuries are pervasive amongst athletes and the military and typically an individual returns to activity prior to fully healing, increasing a predisposition for additional injuries and chronic pain. Monitoring healing progression after a musculoskeletal injury typically involves different types of imaging but these approaches suffer from several disadvantages. Isolating and profiling transcripts from the injured site would abrogate these shortcomings and provide enumerative insights into the regenerative potential of an individual's muscle after injury. In this study, a traumatic injury was administered to a mouse model and healing progression was examined from 3 hours to 1 month using high-throughput RNA-Sequencing (RNA-Seq). Comprehensive dissection of the genome-wide datasets revealed the injured site to be a dynamic, heterogeneous environment composed of multiple cell types and thousands of genes undergoing significant expression changes in highly regulated networks. Four independent approaches were used to determine the set of genes, isoforms, and genetic pathways most characteristic of different time points post-injury and two novel approaches were developed to classify injured tissues at different time points. These results highlight the possibility to quantitatively track healing progression in situ via transcript profiling using high-throughput sequencing.