COMMENTARY Decoding DMD transcriptional networks using single‐nucleus RNA sequencing COMMENTARY Daniel J. Garrya,b,1, Satyabrata Dasa, and Wuming Gonga Duchenne muscular dystrophy (DMD) is an X chromosome-linked disease, and it is the most com- mon form of muscular dystrophy caused by genetic mutations in the Dmd gene (1). The Dmd gene contains 79 exons, spans 2.4 Mb, and is the single largest gene in the human genome (2). This gene encodes for dystro- phin, which is a component of the dystrophin– glycoprotein complex that provides structural stability to the cell membrane (by connecting the cytoskeleton and the extracellular matrix), and a dys- functional or absent dystrophin protein leads to pro- Fig. 1. Decoding the molecular mechanisms of DMD gressive muscle wasting with cycles of muscle pathogenesis utilizing snRNA-seq. This figure schematizes the discoveries outlined in Chemello et al. degeneration and regeneration that ultimately fail (3, (7) with the newly generated DMD mouse model 4). Young boys with DMD lose their ability to ambu- (ΔEx51) and the transcriptomic analysis of isolated late, become wheelchair bound, and die prematurely nuclei from skeletal muscle cells. The snRNA-seq (1, 5). This disease affects skeletal muscle, which is the analysis was used to discover factors and pathways underlying DMD pathogenesis from previously single largest organ in the body, and it normally has a unrecognized cell clusters. These results in remarkable capacity for regeneration (6). In response combination with other databases provide a platform to a severe injury caused by genetic disorders, trauma, for developing new therapeutic interventions or exposure to toxins that destroys over 90% of the mus- as well as an enhanced understanding of the gene-regulatory networks involved in muscle biology cle, the cytoarchitecture of the injured tissue is com- and DMD. pletely restored and is indistinguishable compared to uninjured muscle within a 2- to 4-wk period. This regenerative capacity is due to the myogenic stem mouse model is an important contribution to the cell population (i.e., satellite cells) that is resident in muscular dystrophy research field and will comple- adult skeletal muscle, and with repeated DMD- ment the extensively used, mdx mouse model (8, 9). mediated degeneration and regeneration this re- The mdx mice were discovered in the 1980s and generative process ultimately is exhausted (6). found to have a myopathy due to a spontaneous non- While a number of studies have enhanced our un- sense point mutation causing a stop codon in exon derstanding of the myogenic stem cell population, 23 of the Dmd gene resulting in an absence of dys- new insights are needed regarding the factors that trophin (8). While the mdx mice are extensively used govern muscle regeneration and potentially serve as in the research laboratory, they do not mimic the hu- therapies for diseases like DMD. In PNAS, Chemello man course of disease even though there is muscle et al. (7) use emerging technologies to provide a degeneration, impaired motor performance, and discovery science platform. First, they engineered a mildly reduced life span. The diaphragm is the amousemodelbydeletingexon51oftheDmd only muscle in the mdx mouse that displays the re- gene. This gene-edited mouse (ΔEx51) was viable, markable and progressive myopathic degeneration lacked dystrophin in skeletal muscle and heart, and that mirrors the human disease (9). In contrast, the displayed morphological and physiological deficits utrophin–dystrophin double-mutant mouse model consistent with the DMD phenotype (7). The ΔEx51 has a much more severe muscle disease and a aDepartment of Medicine, University of Minnesota, Minneapolis, MN 55455; and bPaul and Sheila Wellstone Muscular Dystrophy Center, University of Minnesota, Minneapolis, MN 55455 Author contributions: D.J.G., S.D., and W.G. wrote the paper. Competing interest statement: D.J.G. is a cofounder of NorthStar Genomics. Published under the PNAS license. See companion article, “Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing,” 10.1073/pnas.2018391117. 1To whom correspondence may be addressed. Email: [email protected]. www.pnas.org/cgi/doi/10.1073/pnas.2022205117 PNAS Latest Articles | 1of3 Downloaded by guest on September 28, 2021 markedly abbreviated life span and may have an even more se- voluminous amount of data that typically requires bioinformatics vere disease than humans with DMD (10, 11). As is the case expertise. with a number of genetic disorders, including DMD, a major chal- Chemello et al. (7) used snRNA-seq from the TA muscle of lenge for the field is to generate an animal model that reflects the 4-wk-old wild-type (WT) and ΔEx51 mice. The TA muscle clinical manifestations of the human disease. Therefore, future was selected as it is commonly analyzed in the research studies will be needed to fully characterize the ΔEx51 mouse community and has a mixed fiber composition (slow twitch model as it ages. Nevertheless, it will be a valuable resource oxidative and fast twitch glycolytic fibers). They sequenced to the research community as its mutation is more commonly 11,000 nuclei (WT and ΔEx51) and analyzed more than observed in humans and therefore will be an important preclin- 20,000 genes in the WT and dystrophic TA muscles. Based ical model for gene-editing repair and gene therapy initiatives on transcriptional signatures, 14 different clusters of nuclei (12, 13). were identified and supported a high-quality dataset (Fig. 1) (7). Among these cell clusters were myogenic stem cells + In order to begin to characterize the ΔEx51 (Pax7 ), myoblasts (Myob vs. Myh3 and Myh8), cells associ- mouse model, Chemello et al. took an unbiased ated with the neuromuscular junction, smooth muscle cells, endothelialcells,fibroblasts,andothers.Inall,thesecellclus- approach and performed single-nucleus RNA ters were subdivided into three groups: nuclei of the regener- sequencing (snRNA-seq) using nuclei isolated ative pathway, nuclei of mononucleated cells, and myonuclei from the tibialis anterior (TA) skeletal muscle. of myofibers (7). These analyses also demonstrated a reduc- tion of mature myonuclei in the ΔEx51 TA muscle com- The repeated cycles of muscle degeneration and regener- pared to the WT control, and the analysis suggested these ation that characterize DMD muscle ultimately exhaust the changes may have been a result of inflammation, fibrosis, or regenerative capacity of the myogenic stem cells and result in muscle regeneration. The snRNA-seq analysis also uncov- the replacement of the muscle with fibrosis (6). In order to ered degenerative pathways including the ubiquitination begin to characterize the ΔEx51 mouse model, Chemello pathway as well as markers associated with atrophy and apopto- et al. took an unbiased approach and performed single- sis in the ΔEx51 muscle (7). Importantly, these studies also nucleus RNA sequencing (snRNA-seq) using nuclei isolated identified a cluster of cells (RegMyon) that was only found from the tibialis anterior (TA) skeletal muscle (7). The power of in the dystrophic muscle. This cluster of cells expressed em- this technology is that it allows a whole-genome analysis of bryonic and perinatal MyHC isoforms and inference of a hier- large cells such as the multinucleated myofibers (this technol- archical trajectory between the three cell clusters was ogy could also be useful to analyze adult neurons, adipocytes, performed (muscle stem cells or MuSc, myoblasts or Myob, cardiomyocytes, or other cell types). These single-cell/nucleus and RegMyon) to examine their fate. A heatmap gene expres- technologies are revolutionizing our understanding of the cel- sion analysis further supported the notion of the three dis- lular composition of tissues and their dynamic interactions in tinct cell populations and defined highly expressed genes in the RegMyon Cluster (Dclk1, Ncam1, and Baiap2l1 in- the unperturbed and stressed states. The use of these tech- volved in muscle regeneration and the transcription fac- nologies has helped uncover previously undefined and rare tors Runx1, Jdp2, and Mef2a) (7). Moreover, enrichment cell populations, and they have successfully defined cell line- analysis of the transcription factor binding sites supported age relationships. These technologies also provide insights into the notion that Jdp2 could bind the promoters of myo- the diversity or heterogeneity of cell types and the complex na- genic regulatory factors and serve as a regulator of muscle ture of their organization in situ. Importantly, they also enhance regeneration. In summary, using snRNA-seq, the authors our understanding of how cell types survive and respond to were able to characterize the molecular phenotype of the dys- changing microenvironments—in short, the results of these tech- trophic muscle, identify a unique cell cluster (RegMyon), nologies have been a game changer. New platforms now al- and identify factors (Jdp2) that may have an important transcrip- low the analysis of thousands or hundreds of thousands of tional role in myogenesis and will require future examination cells. Compared with single-cell RNA-seq (scRNA-seq), the (Fig. 1). snRNA-seq technique addresses tissues that cannot be While the studies presented in Chemello et al. introduce a readily dissociated into single-cell suspension, such as skeletal muscular dystrophy mouse model that involves a mutation muscle, and it reduces the gene expression variation that may commonly seen in DMD patients and outlines a number of be caused by dissociation. Thus, snRNA-seq technology en- discoveries, future studies and efforts will be essential (Fig. 1) ables the discovery of new cell types that would be difficult to (7). The molecular (snRNA-seq) characterization of muscle groups (the diaphragm, which is commonly affected in isolate. Moreover, recent studies found that snRNA-seq has DMD, slow twitch oxidative muscles, and fast twitch glycolytic superior performance for sensitivity and classification of com- muscles) will need to be expanded and examined at multiple plex cell types (14). Therefore, these advancements are further timeperiodsastheΔEx51 mouse model ages.