GSE147127
GSE GEOSingle-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers
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Summary
While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the emergence of distinct myonuclear populations in postnatal development and their reactivation in aging muscle. Our datasets also provided a platform for discovery of novel genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.
Overall Design
Single-nucleus RNA-seq of tibialis anteior muscle of BL6 wild-type mice across the lifespan: 10 days, 21 days, 5 months (TA and soleus), 24 months, 30 months
Analysis (5 steps)
View Data Processing- Raw sequencing data of all samples were processed using the cellRanger workflow (version 3.1.0), using a combined intron-exon reference produced as described using the vendor-provided âGenerating a Cell Ranger compatible "pre-mRNA" Reference Packageâ guidelines (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references).
- In brief, the âpre-mRNAâ reference was derived using the default exon-level GTF file provided by 10x Genomics (Â http://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-mm10-3.0.0.tar.gz).
- Using the below awk command, this exon-level GTF file into âpre-MRNAâ GTF containing intron transcript definitions).
- Next, the below mkref command was run to produce the final âpre-MRNAâ GTF and genome fasta file ($ cellranger mkref --genome=Mmpre --fasta=genome.fa --genes=genes.premrna.gtf).
- For each dataset, we corrected for ambient background RNA by filtering with the R package SoupX (version 0.3.0), using the inferNonExpressedGenes() function to determine which genes had the highest probability of being ambient mRNA, and the strainCells() function in order to transform count matrices.
Supplementary Files (1)
GEO Samples (6)
Dataset Citations (1)
SRA Experiments (6) and Runs (12)
Total: 155312 MBSample attributes
Original files (1)
Sample attributes
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Linked Publications (1)
Data Files (12)
| Accession | File Name | Stored Type | Output Type | Mapping Assembly | Size | Download | |
|---|---|---|---|---|---|---|---|
| — | 10X-Millay-30-Month-20190812-3v3mm_S6_L001_I1_001.fast… | RNA-Seq | 12.6 GB | link | |||
| — | 10X-Millay-30-Month-20190812-3v3mm_S6_L002_I1_001.fast… | RNA-Seq | 12.6 GB | link | |||
| — | 10X_Millay_Aging_20190326_3v3mm_S3_L001_I1_001.fastq.gz | RNA-Seq | 11.6 GB | link | |||
| — | 10X_Millay_Aging_20190326_3v3mm_S3_L002_I1_001.fastq.gz | RNA-Seq | 11.5 GB | link | |||
| — | 10X-Millay-P10-20191120-3v3mm_S1_L001_I1_001.fastq.gz | RNA-Seq | 19.0 GB | link | |||
| — | 10X-Millay-P10-20191120-3v3mm_S1_L002_I1_001.fastq.gz | RNA-Seq | 19.1 GB | link | |||
| — | 10X-Millay-P21-20190513-3v3mm_S10_L001_I1_001.fastq.gz | RNA-Seq | 11.7 GB | link | |||
| — | 10X-Millay-P21-20190513-3v3mm_S10_L002_I1_001.fastq.gz | RNA-Seq | 11.7 GB | link | |||
| — | 10X-Millay-Soleus-20190826-3v3mm_S6_L001_I1_001.fastq.… | RNA-Seq | 9.7 GB | link | |||
| — | 10X-Millay-Soleus-20190826-3v3mm_S6_L002_I1_001.fastq.… | RNA-Seq | 9.7 GB | link | |||
| — | 10X-Millay-WT-Muscle-20190131-3v3_S10_L001_I1_001.fast… | RNA-Seq | 11.2 GB | link | |||
| — | 10X-Millay-WT-Muscle-20190131-3v3_S10_L002_I1_001.fast… | RNA-Seq | 11.2 GB | link |