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In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing.

Aguilar CA, Shcherbina A, Ricke DO, Pop R, Carrigan CT, Gifford CA, Urso ML, Kottke MA, Meissner A - Sci Rep (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Massachusetts Institute of Technology - Lincoln Laboratory, Lexington, MA 02127, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Global transcriptional dynamics after traumatic muscle injury.(a) Schematic depicting injury to tibialis anterior (TA) muscle (highlighted in red) and bottom inset shows times after injury when muscles were harvested. (b) Heatmap of genes with FPKM >1 at one or more time points categorized into three time periods (early, middle and late). Genes were clustered by their fold change expression profiles in each period. (c) The Venn diagram illustrates the number of significant genes at each time period. For example, there were 139 genes with a significant fold change only at 10 h, 23 genes with a significant fold change only at 24 h, 15 genes with a significant fold change at 10 h, 24 h, and nowhere else, and 4 genes with a significant fold change at 3 h, 10 h, 24 h and nowhere else.
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f1: Global transcriptional dynamics after traumatic muscle injury.(a) Schematic depicting injury to tibialis anterior (TA) muscle (highlighted in red) and bottom inset shows times after injury when muscles were harvested. (b) Heatmap of genes with FPKM >1 at one or more time points categorized into three time periods (early, middle and late). Genes were clustered by their fold change expression profiles in each period. (c) The Venn diagram illustrates the number of significant genes at each time period. For example, there were 139 genes with a significant fold change only at 10 h, 23 genes with a significant fold change only at 24 h, 15 genes with a significant fold change at 10 h, 24 h, and nowhere else, and 4 genes with a significant fold change at 3 h, 10 h, 24 h and nowhere else.

Mentions: Administration of a freeze injury to the tibialis anterior (TA) muscle of ten-week old C57BL/6 mice18, provides a well-studied model of tissue repair after an acute LLMI. Briefly, a 6 mm steel probe, cooled to −70 °C, was applied to the exposed TA muscle and the leg was sutured. The injured muscle was then harvested at several time points after the injury (3, 10, 24, 48, 72, 168, 336, 504, 672 h) and the uninjured contralateral TA muscle was also extracted to serve as a control for each time point (Fig. 1a). Both injured and uninjured tissue samples at each time point were used to perform histological analysis (Supp. Fig. 1). Quantitative imaging of the stained (Evans blue dye) injured and uninjured tissues revealed a gradual increase in tissue damage until 48 h (Supp. Fig. 1b–c), indicating secondary damage to the muscle tissue occurred after the initial cryo-injury, which is consistent with previous studies718.


In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing.

Aguilar CA, Shcherbina A, Ricke DO, Pop R, Carrigan CT, Gifford CA, Urso ML, Kottke MA, Meissner A - Sci Rep (2015)

Global transcriptional dynamics after traumatic muscle injury.(a) Schematic depicting injury to tibialis anterior (TA) muscle (highlighted in red) and bottom inset shows times after injury when muscles were harvested. (b) Heatmap of genes with FPKM >1 at one or more time points categorized into three time periods (early, middle and late). Genes were clustered by their fold change expression profiles in each period. (c) The Venn diagram illustrates the number of significant genes at each time period. For example, there were 139 genes with a significant fold change only at 10 h, 23 genes with a significant fold change only at 24 h, 15 genes with a significant fold change at 10 h, 24 h, and nowhere else, and 4 genes with a significant fold change at 3 h, 10 h, 24 h and nowhere else.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585378&req=5

f1: Global transcriptional dynamics after traumatic muscle injury.(a) Schematic depicting injury to tibialis anterior (TA) muscle (highlighted in red) and bottom inset shows times after injury when muscles were harvested. (b) Heatmap of genes with FPKM >1 at one or more time points categorized into three time periods (early, middle and late). Genes were clustered by their fold change expression profiles in each period. (c) The Venn diagram illustrates the number of significant genes at each time period. For example, there were 139 genes with a significant fold change only at 10 h, 23 genes with a significant fold change only at 24 h, 15 genes with a significant fold change at 10 h, 24 h, and nowhere else, and 4 genes with a significant fold change at 3 h, 10 h, 24 h and nowhere else.
Mentions: Administration of a freeze injury to the tibialis anterior (TA) muscle of ten-week old C57BL/6 mice18, provides a well-studied model of tissue repair after an acute LLMI. Briefly, a 6 mm steel probe, cooled to −70 °C, was applied to the exposed TA muscle and the leg was sutured. The injured muscle was then harvested at several time points after the injury (3, 10, 24, 48, 72, 168, 336, 504, 672 h) and the uninjured contralateral TA muscle was also extracted to serve as a control for each time point (Fig. 1a). Both injured and uninjured tissue samples at each time point were used to perform histological analysis (Supp. Fig. 1). Quantitative imaging of the stained (Evans blue dye) injured and uninjured tissues revealed a gradual increase in tissue damage until 48 h (Supp. Fig. 1b–c), indicating secondary damage to the muscle tissue occurred after the initial cryo-injury, which is consistent with previous studies718.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Massachusetts Institute of Technology - Lincoln Laboratory, Lexington, MA 02127, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus