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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

Results from various bioinformatics classification schemes utilized to analyze transcriptomic datasets show accurate categorization of injured and uninjured samples.(a) One-versus-one support vector classification results for test samples. A test sample was analyzed with 45 classifiers, each of which assigned the sample to one of two time points. Voting was used to group classifier results. The height of the bars indicates the number of votes given to each time point for a given sample. Top graph displays classification results for injured samples – 2 samples from 3h after injury, 2 samples for 10 h, 2 samples for 168 h. Bottom graph displays classification of results for 6 control samples. (b) Principal component analysis clustering of 12 test samples at the gene level. 66 training samples and 12 test samples are plotted in the space of principal components 1 and 2. Labels specify the time point of the nearest training sample for each of the test samples. Misclassified samples are circled in red. All other sample classifications were correct. (c) Similarity profiles of training and test samples to the control data and each of the 9 injured time points. Truth sample profiles are indicated in blue. If a scored sample and a truth sample for a given time point both exhibit a fold change for a gene, or if both exhibit no fold change for the gene, the score is incremented. The score increment is equal to the normalized fold change (on a scale from 0 to 1) in the truth sample relative to a control, or 0.5 if both sample exhibit no fold change.
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f6: Results from various bioinformatics classification schemes utilized to analyze transcriptomic datasets show accurate categorization of injured and uninjured samples.(a) One-versus-one support vector classification results for test samples. A test sample was analyzed with 45 classifiers, each of which assigned the sample to one of two time points. Voting was used to group classifier results. The height of the bars indicates the number of votes given to each time point for a given sample. Top graph displays classification results for injured samples – 2 samples from 3h after injury, 2 samples for 10 h, 2 samples for 168 h. Bottom graph displays classification of results for 6 control samples. (b) Principal component analysis clustering of 12 test samples at the gene level. 66 training samples and 12 test samples are plotted in the space of principal components 1 and 2. Labels specify the time point of the nearest training sample for each of the test samples. Misclassified samples are circled in red. All other sample classifications were correct. (c) Similarity profiles of training and test samples to the control data and each of the 9 injured time points. Truth sample profiles are indicated in blue. If a scored sample and a truth sample for a given time point both exhibit a fold change for a gene, or if both exhibit no fold change for the gene, the score is incremented. The score increment is equal to the normalized fold change (on a scale from 0 to 1) in the truth sample relative to a control, or 0.5 if both sample exhibit no fold change.

Mentions: An SVM classifier was developed and the best performance was obtained when the data was filtered to include all significant genes (see Methods). The weighted SVM calls from each pair of classifiers were summed, and the time point with the highest number of weighted votes was designated as the final classification call. The performance of the SVM classifier is illustrated in Fig. 6a, whereby the positive or negative symbol over each graph represents if the SVM call was accurate (positive symbol) or inaccurate (negative symbol). The relative height of the bars can be analyzed further to uncover generalizable patterns of performance. As can be seen for the uninjured control datasets, the height of the 10 h bars is slightly lower than the height of the control bars for four datasets. Similarly, for the injured 3 h datasets, the height of the uninjured control bars is slightly lower than the height of the injured 3 h bars. This result demonstrates that the SVM call possessed high confidence since adjacent time points both have a high number of votes. Overall, these results demonstrate that the SVM classifier could accurately identify 75% of the test datasets, but other bioinformatics techniques were evaluated to determine if higher classification accuracy could be obtained.


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)

Results from various bioinformatics classification schemes utilized to analyze transcriptomic datasets show accurate categorization of injured and uninjured samples.(a) One-versus-one support vector classification results for test samples. A test sample was analyzed with 45 classifiers, each of which assigned the sample to one of two time points. Voting was used to group classifier results. The height of the bars indicates the number of votes given to each time point for a given sample. Top graph displays classification results for injured samples – 2 samples from 3h after injury, 2 samples for 10 h, 2 samples for 168 h. Bottom graph displays classification of results for 6 control samples. (b) Principal component analysis clustering of 12 test samples at the gene level. 66 training samples and 12 test samples are plotted in the space of principal components 1 and 2. Labels specify the time point of the nearest training sample for each of the test samples. Misclassified samples are circled in red. All other sample classifications were correct. (c) Similarity profiles of training and test samples to the control data and each of the 9 injured time points. Truth sample profiles are indicated in blue. If a scored sample and a truth sample for a given time point both exhibit a fold change for a gene, or if both exhibit no fold change for the gene, the score is incremented. The score increment is equal to the normalized fold change (on a scale from 0 to 1) in the truth sample relative to a control, or 0.5 if both sample exhibit no fold change.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4585378&req=5

f6: Results from various bioinformatics classification schemes utilized to analyze transcriptomic datasets show accurate categorization of injured and uninjured samples.(a) One-versus-one support vector classification results for test samples. A test sample was analyzed with 45 classifiers, each of which assigned the sample to one of two time points. Voting was used to group classifier results. The height of the bars indicates the number of votes given to each time point for a given sample. Top graph displays classification results for injured samples – 2 samples from 3h after injury, 2 samples for 10 h, 2 samples for 168 h. Bottom graph displays classification of results for 6 control samples. (b) Principal component analysis clustering of 12 test samples at the gene level. 66 training samples and 12 test samples are plotted in the space of principal components 1 and 2. Labels specify the time point of the nearest training sample for each of the test samples. Misclassified samples are circled in red. All other sample classifications were correct. (c) Similarity profiles of training and test samples to the control data and each of the 9 injured time points. Truth sample profiles are indicated in blue. If a scored sample and a truth sample for a given time point both exhibit a fold change for a gene, or if both exhibit no fold change for the gene, the score is incremented. The score increment is equal to the normalized fold change (on a scale from 0 to 1) in the truth sample relative to a control, or 0.5 if both sample exhibit no fold change.
Mentions: An SVM classifier was developed and the best performance was obtained when the data was filtered to include all significant genes (see Methods). The weighted SVM calls from each pair of classifiers were summed, and the time point with the highest number of weighted votes was designated as the final classification call. The performance of the SVM classifier is illustrated in Fig. 6a, whereby the positive or negative symbol over each graph represents if the SVM call was accurate (positive symbol) or inaccurate (negative symbol). The relative height of the bars can be analyzed further to uncover generalizable patterns of performance. As can be seen for the uninjured control datasets, the height of the 10 h bars is slightly lower than the height of the control bars for four datasets. Similarly, for the injured 3 h datasets, the height of the uninjured control bars is slightly lower than the height of the injured 3 h bars. This result demonstrates that the SVM call possessed high confidence since adjacent time points both have a high number of votes. Overall, these results demonstrate that the SVM classifier could accurately identify 75% of the test datasets, but other bioinformatics techniques were evaluated to determine if higher classification accuracy could be obtained.

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