Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity

Abstract

Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.

Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei

“…We used two independent primary control myoblast samples, Control-1 and Control-2, and two independent primary FSHD2 myoblast samples, FSHD2-1 and FSHD2-2, which have known SMCHD1 mutations. In addition, we compared our time-course to the differentiation of the immortalized human myoblast cell line KD3 [23] which was used previously to study the fate heterogeneity during myotube differentiation by single-nucleus RNA-seq [21]. After sequencing two biological replicate RNA samples for each of the five cell populations every day for six days, we filtered out lowly expressed genes across all 60 samples and performed principal component analysis (PCA) with 10,767 genes ( Figure 1B).…”

“…We found that about 40% of differentially expressed genes are known FSHD-associated genes while the others may be potential new candidates involved in the progression of the disease. We then used single-cell RNA-seq in myoblasts and single-nucleus RNA-seq [21] in 3-day postdifferentiation myotubes to characterize the expression patterns of DUX4 and other FSHDinduced genes. We sucessfully detected the first set of single nuclei with DUX4 expression (DUX4-detected) from FSHD myotubes and found that they do not express all the FSHDinduced genes whereas a much larger set of FSHD myotube nuclei express FSHD-induced genes.…”

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

“…Thus, existing high-throughput single-cell capture and library preparation methods, including isolation of cells by fluorescence activated cell sorting (FACS) into multi-well plates, sub-nanoliter wells, or droplet microfluidic encapsulation, are not optimized to accommodate these unusually large cells. Isolating individual nuclei for transcriptome analysis is a promising strategy, as single-nucleus RNA-Seq methods avoid strong biases against cells of complex morphology and large size (Habib et al, 2016; Lacar et al, 2016; Lake et al, 2016; Zeng et al, 2016) and can be potentially standardized to accommodate the study of various tissues. However, current single-nucleus RNA-Seq methods primarily rely on fluorescence-activated nuclei sorting (FANS) (Habib et al, 2016; Lake et al, 2016) or Fluidigm C1 microfludics platform (Zeng et al, 2016) to capture nuclei, and thus cannot easily be scaled up to generate a comprehensive atlas of cell types in a given tissue, much less a whole organism.…”

Comprehensive single-cell transcriptional profiling of a multicellular organism

“…Recently, profiling the transcriptome of individual cells has emerged as a powerful strategy for resolving such heterogeneity. The expression levels of mRNA species are linked to cellular function, and therefore can be used to classify cell types (2–10) and to order cell states (11). Although methods for single cell RNA-seq have proliferated, they rely on the isolation of individual cells within physical compartments (12–20).…”