2020
DOI: 10.1038/s41598-019-57110-6
|View full text |Cite
|
Sign up to set email alerts
|

Single-cell transcriptional profiles in human skeletal muscle

Abstract: Skeletal muscle is a heterogeneous tissue comprised of muscle fiber and mononuclear cell types that, in addition to movement, influences immunity, metabolism and cognition. We investigated the gene expression patterns of skeletal muscle cells using RNA-seq of subtype-pooled single human muscle fibers and single cell RNA-seq of mononuclear cells from human vastus lateralis, mouse quadriceps, and mouse diaphragm. We identified 11 human skeletal muscle mononuclear cell types, including two fibro-adipogenic progen… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

27
225
0
2

Year Published

2020
2020
2024
2024

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 219 publications
(263 citation statements)
references
References 70 publications
27
225
0
2
Order By: Relevance
“…Discussion Here we present snATAC-seq and snRNA-seq for human skeletal muscle and snATAC-seq for rat skeletal muscle, which we use to map the transcriptomes and chromatin accessibility of cell types present in skeletal muscle samples. The cell types identified are consistent with known biology and with previous studies of human (13) and mouse (16,17,20) skeletal muscle tissue. However, our use of single-nucleus rather than single-cell techniques allows us to capture muscle fiber nuclei, cell types missing from previously published snRNA-seq datasets.…”
Section: Integration Of Cell Type-specific Atac-seq Peaks With T2d Gwsupporting
confidence: 86%
See 1 more Smart Citation
“…Discussion Here we present snATAC-seq and snRNA-seq for human skeletal muscle and snATAC-seq for rat skeletal muscle, which we use to map the transcriptomes and chromatin accessibility of cell types present in skeletal muscle samples. The cell types identified are consistent with known biology and with previous studies of human (13) and mouse (16,17,20) skeletal muscle tissue. However, our use of single-nucleus rather than single-cell techniques allows us to capture muscle fiber nuclei, cell types missing from previously published snRNA-seq datasets.…”
Section: Integration Of Cell Type-specific Atac-seq Peaks With T2d Gwsupporting
confidence: 86%
“…The majority of the nuclei were assigned as type I or type II muscle fibers. Genes previously discovered to be preferentially expressed in type I vs. type II muscle fibers (13) were usually similarly preferentially expressed in our snRNA-seq data ( Fig. S11), validating the quality of the data and accuracy of muscle fiber type assignments.…”
Section: Joint Clustering Of Human and Rat Snatac-seq And Snrna-seq Isupporting
confidence: 85%
“…Given the complexity of human skeletal muscle tissue involving multinucleated muscle fibers, immune cells, endothelial cells, muscle stem cells, non-myogenic mesenchymal progenitors, and other mononuclear cell (Bentzinger et al, 2013a), future research would be needed to elucidate the contribution of each of these cells to the structure and remodeling of the IMCT. Gene signatures derived, e.g., from RNA-seq of isolated muscle fibers and other cell types comprise a promising tool in the deconvolution of bulk skeletal muscle tissue (Rubenstein et al, 2020).…”
Section: Composition and Structure Of Skeletal Muscle Ecmmentioning
confidence: 99%
“…MSC are found in most tissues, these cells are capable of multipotent differentiation into bone, muscle, cartilage, adipocytes, marrow stromal cells, tenocytes, fibroblasts, endothelial and neural cells (Caplan 2007;Pittenger et al 2019). Tissues maintain a pool of MSC, with varying degrees of specialization, ready to dynamically replenish differentiated cells in response to signals associated with growth, homeostasis or damage (Rubenstein et al 2020). Prior to this study, ASPL-TFE3 had already been shown to immortalize embryonic mesenchymal cells (Tanaka et al 2017).…”
Section: Resultsmentioning
confidence: 99%