Molecular Imaging to Target Transplanted Muscle Progenitor Cells
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a severe genetic neuromuscular disorder that affects 1 in 3,500 boys, and is characterized by progressive muscle degeneration. In patients, the ability of resident muscle satellite cells (SCs) to regenerate damaged myofibers becomes increasingly inefficient. Therefore, transplantation of muscle progenitor cells (MPCs)/myoblasts from healthy subjects is a promising therapeutic approach to DMD. A major limitation to the use of stem cell therapy, however, is a lack of reliable imaging technologies for long-term monitoring of implanted cells, and for evaluating its effectiveness. Here, we describe a non-invasive, real-time approach to evaluate the success of myoblast transplantation. This method takes advantage of a unified fusion reporter gene composed of genes (firefly luciferase [fluc], monomeric red fluorescent protein [mrfp] and sr39 thymidine kinase [sr39tk]) whose expression can be imaged with different imaging modalities. A variety of imaging modalities, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and high frequency 3D-ultrasound are now available, each with unique advantages and limitations. Bioluminescence imaging (BLI) studies, for example, have the advantage of being relatively low cost and high-throughput. It is for this reason that, in this study, we make use of the firefly luciferase (fluc) reporter gene sequence contained within the fusion gene and bioluminescence imaging (BLI) for the short-term localization of viable C2C12 myoblasts following implantation into a mouse model of DMD (muscular dystrophy on the X chromosome [mdx] mouse). Importantly, BLI provides us with a means to examine the kinetics of labeled MPCs post-implantation, and will be useful to track cells repeatedly over time and following migration. Our reporter gene approach further allows us to merge multiple imaging modalities in a single living subject; given the tomographic nature, fine spatial resolution and ability to scale up to larger animals and humans, PET will form the basis of future work that we suggest may facilitate rapid translation of methods developed in cells to preclinical models and to clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".