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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

Sample classification results from four bioinformatics classification methods—support vector machine with linear kernel (blue arrows), principal component analysis (orange arrows), time point weighted signatures method (red arrows), time point-specific signatures method (green arrows).The arrows indicate the time point reported by each of four methods with highest confidence. Twelve blinded samples, corresponding to four time points, were analyzed: 6 control samples, two 3 h samples, two 10 h samples, and two 168 h samples.
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f7: Sample classification results from four bioinformatics classification methods—support vector machine with linear kernel (blue arrows), principal component analysis (orange arrows), time point weighted signatures method (red arrows), time point-specific signatures method (green arrows).The arrows indicate the time point reported by each of four methods with highest confidence. Twelve blinded samples, corresponding to four time points, were analyzed: 6 control samples, two 3 h samples, two 10 h samples, and two 168 h samples.

Mentions: The overall performance of the four sample classification methods is illustrated in Fig. 7. All of the methods incorrectly predicted one of the injured 168 h samples as either injured 336 h (both the Time point-Weighted and Time point-Specific methods) or as an uninjured control (PCA and SVM methods). The other injured 168 h test sample was also incorrectly predicted by 3 of the methods. This result is due to variability in the training data from the 168 h injured samples. Overall, the results from these classification schemas suggest that the time point signature approaches outperformed the SVM and PCA classifiers by 10 percent, with time point weighted signatures method performing better than time point- specific signatures method.


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)

Sample classification results from four bioinformatics classification methods—support vector machine with linear kernel (blue arrows), principal component analysis (orange arrows), time point weighted signatures method (red arrows), time point-specific signatures method (green arrows).The arrows indicate the time point reported by each of four methods with highest confidence. Twelve blinded samples, corresponding to four time points, were analyzed: 6 control samples, two 3 h samples, two 10 h samples, and two 168 h samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: Sample classification results from four bioinformatics classification methods—support vector machine with linear kernel (blue arrows), principal component analysis (orange arrows), time point weighted signatures method (red arrows), time point-specific signatures method (green arrows).The arrows indicate the time point reported by each of four methods with highest confidence. Twelve blinded samples, corresponding to four time points, were analyzed: 6 control samples, two 3 h samples, two 10 h samples, and two 168 h samples.
Mentions: The overall performance of the four sample classification methods is illustrated in Fig. 7. All of the methods incorrectly predicted one of the injured 168 h samples as either injured 336 h (both the Time point-Weighted and Time point-Specific methods) or as an uninjured control (PCA and SVM methods). The other injured 168 h test sample was also incorrectly predicted by 3 of the methods. This result is due to variability in the training data from the 168 h injured samples. Overall, the results from these classification schemas suggest that the time point signature approaches outperformed the SVM and PCA classifiers by 10 percent, with time point weighted signatures method performing better than time point- specific signatures method.

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