#apaperaday: Transcriptomic gene signatures measure satellite cell activity in muscular dystrophies

In today’s #apaperaday, Prof. Aartsma-Rus reads and comments on the paper titled: Transcriptomic gene signatures measure satellite cell activity in muscular dystrophies

Today’s pick is from ⁦@iScience_CP ⁩ by Engquist et al on gene signatures of muscle satellite cells doi 10.1016/j.isci.2024.109947

Muscle fibers grow and repair themselves with satellite cells. These are inactive stem cells that lie quiescent on top of fibers. When activated they proliferate and fuse with (damaged) muscle fibers and part renews and go back to quiescent state.

In muscular dystrophy there is constant muscle damage and thus the satellite cell and repair mechanisms are stressed. Authors outline mutations that impair satellite cell function and proliferation cause muscle diseases.

However muscle diseases where satellite cells are not directly affected still have an impact on satellite cell function as the environment will be inflamed or fibrotic or adipogenic. This will thus impair repair and exacerbate pathology.

Single cell sequencing can show signatures of individual cell types. However, satellite cells are sparse and thus satellite cells are often not present in single cell sequencing datasets. Thus it is difficult to see the expression signature for satellite cells.

Here authors used large mouse single cell datasets to create expression signatures for satellite cells, myoblasts and myo(fiber) nuclei (see image). They then checked whether the signatures were correct in bulk RNA sequencing datasets of injured rat muscle.

They also compared patterns in human muscle samples comparing bulk RNA and histology and the signatures, which suggested the signatures were translating from mouse to rat to human although in human samples they could not detect myoblasts due to low numbers.

Finally authors tested patient derived samples with bulk RNA and saw that in inflamed FSHD muscles (as assessed by MRI) there were higher expression levels of signature genes for satellite cells and myoblasts than non inflamed ones.

For Duchenne they saw increased myoblast and satellite cell signatures and for DM1 they saw increased myoblast signatures while there were too little genes expressed from the satellite cell signature to draw conclusions.

Authors discuss that they used genes only expressed in the sub populations for the signatures so eg MyoD is not present. They admit that a limitation is that they started with a mouse dataset. However if the signatures translate between species that may actually be good.

One limitation of the signatures is that when these genes or not detected at sufficient levels in bulk RNA it is difficult to draw conclusions. Also I think that using this method does not allow you to know the number of cells contributing to a pattern.

You may assume they all contribute equally but this may not be true in a pathological state. Also changes within myoblasts – eg towards FAP (fibro adipogenic precursors) will be missed in bulk RNA.