Single-cell sequencing deconvolutes cellular responses to exercise in human skeletal muscle

Clinical characterization

Three healthy, moderately active individuals (Table 1) performed three 30 s all-out sprint exercises with 2 min of recovery between sprints. Peak power output in the three sprints was on average 8.87 ± 2 W/kg, corresponding to 10.3 metabolic equivalents (METs). Total energy expenditure was 14.5 ± 6.9 KJ.

Table 1 Study participants.

Cellular yield, composition, and biological processes in different cell populations

The tissue retrieval was 500 ± 150 mg per sample and a total of 39,015 cells from pre- and 24,332 cells from post-exercise were analyzed with a mean read count of 19,507,451 and 19,328 882, respectively. To investigate reproducibility and increase sequence-depth, cells isolated from the third subject were sequenced twice.

Cell-type annotation and reproducibility

The six samples from the three subjects, in addition to the two re-sequenced samples from the third subject, were integrated, anchored, and clustered (Fig. 1a). Inter-sample reproducibility with regards to cell-type composition was assessed by a correlation matrix using inter-sample anchoring scores17. Agreement both between and within subject was consistent, with anchoring scores ranging from 0.6 to 0.7 (Fig. 1b). All subjects contained cells of all cell types detected (Fig. 1c), with comparable proportions between samples (Fig. 1d). Thus, the cell-type annotations and anchoring were highly reproducible across different subjects and biopsies. Sample distribution across clusters is found in Supplementary Figure 1. Following Leiden-based clustering and cell-type analysis, six major cell types were identified: myogenic cells, endothelial cells, pericyte cells, mesenchymal cells, lymphoid cells, and monocyte cells. Cell-type annotation was based on a combination of marker gene expression compared to the CellMatch database, the scCatch pipeline18, and an enrichment test of cell types by using highly expressed/marker genes in each cluster relative to the transcriptomic profiles of cell types provided by the Human Gene Atlas19,20. All populations, except the pericyte cluster, were consistently annotated by ≥2 of the methods and were therefore considered unambiguously annotated with respect to cellular origin. Composition tables for individual samples can be found in Supplementary Data 1.

Fig. 1: Data alignment and reproducibility.
figure 1

Reproducibility of scRNA-seq between and within subjects. a A total of 63 000 cells isolated from six vastus lateralis muscle biopsies from three different subjects were processed, clustered, and visualized using UMAP projection, with color coding indicating the contributing donor and sample. Cells aligned into six major cell populations in which cells from all donors were evenly distributed, providing a measure of reproducibility of cellular composition. b This was further confirmed by consistent anchoring scores, a measure of correlation between samples, both within and between subjects, as shown in the correlation-matrix. c Cell-type annotation identified six different cell types/lineages as indicated on the UMAP. d Cell-type composition in all subjects at baseline, including resequencing of one subject to confirm reproducibility within and between samples. Endothelial cells were the most abundant cell type constituting 44% of the samples followed by mesenchymal 26% and myogenic cells at 18%. Complete composition can be found in Supplementary Data 1. e Based on the sequencing data from homogenized whole tissue, 5844 transcripts were identified as part of the skeletal muscle transcriptome and 1946 unique transcripts were detected with the scRNA-seq. A substantial proportion (504 or 26%) of these transcripts were detected only with the scRNA-seq. f The unique transcripts from scRNA-seq analysis were highly enriched in genes from endothelial, immune, and stem cell populations, suggesting that scRNA-seq captures a transcriptional signature of cell populations that otherwise goes largely undetected when using RNA-seq strategies.

Comparison with whole-tissue RNA-sequencing

An obvious advantage of scRNA-seq over whole-tissue RNA-sequencing (RNA-seq) is the ability to deconvolute gene expression profiles into their respective cell-type-based compartments. In addition, it is also possible to provide better coverage of gene expression of less abundant cell types, such as immune and stem cells. To address and objectify this notion, genes detected in the present single-cell experiment were compared with the comprehensive profile of the skeletal muscle transcriptome available through the Genotype-Tissue Expression data (GTEx)21. In the 564 skeletal muscle samples from the GTEx study, 5844 unique transcripts were detected readably, while in the current single-cell experiment a total of 1946 unique transcripts were detected. Of the transcripts detected by scRNA-seq, 504 transcripts were not readably detected in the GTEx data, representing 26% of all the transcripts identified by scRNA-seq (Fig. 1e). Among transcripts detected exclusively in the single-cell experiment, gene and cell ontology analysis revealed strong enrichment of genes derived from immune, endothelial, mesenchymal, and smooth muscle cells (Fig. 1f). Among the differentially enriched biological processes associated with these cell populations were “positive regulation of T cell proliferation” (fdr < 0.01), “positive regulation of angiogenesis” (fdr < 0.01), and “positive regulation of endothelial cell proliferation” (fdr < 0.05), indicative of cell type-specific enrichments (Fig. 1f).

Cellular composition and cell-type characteristics

Endothelial cells accounted for 44% of the total number of cells and were distributed in four distinct but neighboring clusters (ESAM+, VWF+/EPS8+, ACKR1+, and FABP4+/NEAT1−) (Fig. 2a). Overall, endothelial cells were characterized by high expression of known endothelial marker genes, including VWF and ESAM (Fig. 2c, d). In comparison with the other cell types, endothelial cells had a significantly higher expression of 165 genes and enriched for 263 gene ontologies such as “regulation vasculature development” (fdr < 0.05), “actin filament organization” (fdr < 0.01), and “response to wounding” (fdr < 0.001) (Fig. 2b). More detailed tables of cell-type and subpopulation specific differential expression together with ontology enrichment can be found in the Supplemental Information.

Fig. 2: Cell-type and subpopulations.
figure 2

Marker expression, and ontology enrichment across the different cell types and subpopulations. a Cell-type annotation was based on a combination of marker gene expression compared with the CellMatch database in relation to profiles of specific cell types in the Human Gene Atlas. Six different cell types/lineages were identified and color-coded on the UMAP: Endothelial cells, Myogenic cells, Mesenchymal cells, Myeloid cells, Lymphoid cells, and Pericyte cells. Distinct cell clusters derived from the same cell type were compared and characterized through differential expression. Four subpopulations of endothelial cells were identified to differ by the expression of VWF+/EPS8+, ESAM+, and FABP4+/NEAT−, respectively. There were three mesenchymal subpopulations characterized by differential expression of DCN+/CFD+, GSN+/LUM+, and S100A8+/S100A9+, and muscle cells could be further subdivided into three myogenic subpopulations, including PAX7+ satellite/myoblast cells, and fast- (TNNI2+) and slow-twitch (TNNI1+) troponin expressing cells. Pericytes characterized by high/marker gene expression of ACTA2 and PDGFR were divided into two subpopulations (NDUFA4L2+ and TAGLN+). One monocyte population was found to express CD14. b Enrichment analysis of biological functions in the different cell populations shows functionally different gene expression profiles for the different cell types. Color coding represents different cell-type and larger dot radius denotes more significant biological function (fdr < 0.05). c Marker gene expression in the different cell populations. Red and gray colors indicate the expression of each marker gene above and below hard threshold in all cells, respectively. d Quantitative comparison of the expression of the marker genes in the different cell types. The data in boxplots nested within violin plots are expressed as median, interquartile range, minimum and maximum values. Normalized expression refers to log(1 + x) if not stated otherwise. SC satellite cells; ST slow-twitch, FT fast-twitch, cuff cutoff. Complete marker-gene differential expression can be found in Supplementary Data 24.

Mesenchymal cells represented 26% of the total number of cells and showed significantly increased expression of DCN, CFD, and GSN. These cells were further divided into three distinct subpopulations (i.e., DCN+/CFD+, GSN+/LUM+, S100A8+/S100A9+) (Fig. 2a, c, d). The ontologies enriched in the mesenchymal cell population were generic in nature, including “autophagy” (fdr < 0.01), “regulation of anatomical structure morphogenesis” (fdr < 0.01), “oxidative phosphorylation” (fdr < 0.01), “electron transport chain” (fdr < 0.01), and “cell activation” (fdr < 0.05) (Fig. 2b). The S100A8+/S100A9+ subpopulation was considered ambiguously annotated. However, due to the high expression of DCN, VIM, COL6A3, and GSN marker genes the cluster was deemed to be of mesenchymal origin.

Myogenic cells represented 18% of total cells and were further divided into three distinct subpopulations (i.e., PAX7+, TNNI1+, TNNI2+) (Fig. 2a). In addition to PAX7, the first cluster expressed MYF5 and in a smaller frequency MYOD1, indicating this cluster is a mix of undifferentiated satellite cells and early myoblasts. The remaining myogenic subpopulations expressed genes associated with more mature characteristics such as DES, MYL2, and MYOG (Fig. 2c, d), and perhaps terminally differentiated muscle fibers including ACTA1 and MYH6. A subset of these cells also expressed MYOD1. These two subpopulations were distinguished from each other by the expression of slow-twitch (TNNI1) and fast-twitch (TNNI2) troponins. At the ontology level, the myogenic subpopulations exhibited a gene expression profile dominated by key skeletal muscle functions such as “oxidative phosphorylation” (fdr < 0.001) and “mitochondrial respiratory chain” (fdr < 0.001) (Fig. 2b). Consistent with muscle-specific gene expression, the TNNI1 + and TNNI2 + cells also showed enrichment for programs involved in skeletal muscle protein synthesis and degradation (e.g., TRIM63, SYNPO2).

Pericytes accounted for 6% of the total cells, with two distinct clusters (TAGLN+ and NDUFA4L2+) (Fig. 2a). The TAGLN+ cluster was characterized by the high expression of smooth muscle markers such as ACTA2 and pericyte markers (e.g., PDGFR). In addition, this cluster was also annotated as pericyte in origin by scCatch, and therefore pericyte was considered the most probable cellular origin. Significantly enriched ontologies included “mitochondrial electron transport cytochrome c to oxygen” (fdr < 0.001), “nucleoside triphosphate metabolic process” (fdr < 0.001), and “oxidative phosphorylation” (fdr < 0.001) (Fig. 2b).

Two lymphocyte clusters (NAMPT+ and HCST+) were also identified, which accounted for 4% of the total number of cells (Fig. 2a). Considering these clusters presented gene expression profiles characterizing T-, B-, and NK-cells, they were collectively considered as lymphocytes. Genes that distinguished these clusters from the other cell types included CCL5 which is considered as lymphocyte-specific markers (Fig. 2c, d). Ontological enrichment showed “T-cell immunity” (fdr < 0.01), “cell killing” (fdr < 0.05), and “adaptive immune response” (fdr < 0.05).

One monocyte cluster, which accounted for 2% of the total number of cells, was classified as myeloid in origin, based on CD14 expression (Fig. 2a). The cluster differentially expressed genes including CXCL8, AIF, and TYROBP (Fig. 2c, d). HLA-DRA was also highly expressed in this cluster, which is a gene associated with antigen-presenting cells, due to its structural involvement in the formation of human leukocyte antigen (HLA) class II proteins. These CD14+ monocytes enriched for ontologies including “multi organism metabolic process” (fdr < 0.001), “response to corticosterone” (fdr < 0.05), “cellular response to calcium ion” (fdr < 0.05), “response to mineralocorticoid” (fdr < 0.05), “translation initiation” (fdr < 0.001), “Ribosome assembly” (fdr < 0.001), and “Nuclear transcribed mRNA catabolic process nonsense-mediated decay” (fdr < 0.001) (Fig. 2b). Gene-ontology enrichment and marker gene expression for all cell-types can be found in Supplementary Data 2, 3 and 4 and in Supplementary Fig. 2.

Effects of exercise on cellular composition

The effects of high intensity exercise were first examined with respect to cellular composition in skeletal muscle (Fig. 3a). Three hours after exercise, circulating cells increased substantially, with lymphocytes increasing from 4 to 9% (p = 0.05) and monocytes from 2 to 4% (p < 0.05), with a corresponding decrease in the relative contribution of resident cells, i.e., endothelial cells decreased from 44 to 37% and pericytes decreased from 6 to 5% after exercise. The proportion of myogenic cells (18%) remained unchanged after exercise.

Fig. 3: Transcriptional response to a single bout of exercise across cell types.
figure 3

Exercise effects detected by scRNA-seq analysis of the human skeletal muscle. a Cellular composition analysis of samples before (pre-exercise) and three hours after (post-exercise) a single bout of exercise. b Differential expression analysis across cell types after a single bout of exercise. Color coding represents cell-type, and bar height denotes the number of exercise-regulated genes. In total, 874 (535 unique) genes were upregulated by exercise, with mesenchymal cells having the highest number (304), followed by endothelial cells (281) and monocytes (120). In contrast, only 9 genes were found to respond to the exercise stimulus in the lymphocyte population. c Gene-ontology analysis revealed that most biological processes regulated by exercise were cell-type specific, with a small number of processes similarly regulated in most cell types (“mRNA catabolic process” and “nitrogen formation”). Color coding represents different cell-type, and a larger dot radius denotes a more significant gene ontology (fdr < 0.05). dh Normalized expression level of representative genes for each cell type that were differentially expressed after exercise. The data in boxplots nested within violin plots are expressed as median, interquartile range, minimum and maximum values. ***fdr < 10e−5. i Genes that were significantly regulated by exercise in the current scRNA-seq experiment were compared with differentially expressed genes after exercise using RNA-seq from whole muscle. Color coding represents different cell-type, and numerical value denotes the number of differentially expressed genes shared by both methods or those exclusively detected by scRNA-seq. Approximately 25% of the genes detected by scRNA-seq were also identified using RNA-seq and this was consistent across all cell types. Normalized expression refers to log(1 + x) if not stated otherwise. Complete exercise-differential expression and ontology analysis can be found in Supplementary Data 59.

Transcriptional response to exercise

The transcriptional response to exercise was assessed by comparing pre- vs. post-exercise for each cell type separately. A total of 874 (535 unique) genes were differentially expressed (fdr < 0.05) across all different cell types. In terms of number of differentially expressed genes, the mesenchymal cells showed the greatest exercise-related response, with 304 genes differentially expressed (Fig. 3b). In ontological terms, the mesenchymal cells enriched for biological functions involved in tissue regeneration and remodeling, such as “regeneration” (fdr < 0.01), “organ regeneration” (fdr < 0.05), and “wound healing” (fdr < 0.05) (Fig. 3c). Genes driving the enrichment for regeneration biological function in the mesenchymal cells included VIM, UBC, GPX4, and AVCRL1 (Fig. 3d). Several genes involved in cytoskeletal reorganization and cell-cycle activation, such as RHOBTB3, TPM1, and RGCC, were also robustly upregulated after exercise in this cell type.

The endothelial cells showed the second greatest response to exercise with a total of 281 differentially expressed genes (Fig. 3b). The main ontological characterization included cell activation and stress reactions, such as “cell cycle G2 M-phase transition” (fdr < 0.05), “energy reserve metabolic response” (fdr < 0.01), and “tissue regeneration” (fdr < 0.05) (Fig. 3c). Differentially expressed genes such as TIMP3, ACTB, UBC, and CALM1 (Fig. 3e) indicate endothelial re-composition and stress response after exercise.

The myogenic cell populations differentially expressed 111 genes (fdr < 0.05) after exercise, including genes involved in differentiation along myogenic lineage such as MYOD1, and MYF6 (Fig. 4). In the undifferentiated PAX7+ cluster gene ontologies related primarily to cellular stress response, such as “negative regulation of cell death” (fdr < 0.001) and “regulation of growth” (fdr < 0.05) (Fig. 3c). The genes driving the stress-related enrichments for the PAX7+ cluster included UBC, HSP90AB1, LMNA, NCL, and SOD2. Muscle-related functions, such as “actin-mediated cell contraction” (fdr < 0.001), and “muscle system process” (fdr < 0.001), were enriched in the more mature clusters (TNNI1+, and TNNI2+) (Fig. 3c) and mainly driven by gene expression such as DES, ACTA, MYL2, and MYOZ1.

Fig. 4: Myogenic cell trajectories.
figure 4

Three distinct myogenic subpopulations of myogenic cells were identified and further investigated for their distinguishing features and trajectories. a The first cluster (brown) was characterized by PAX7+ expression (satellite/myoblast cells), whereas the remaining two myogenic subpopulations expressed higher levels of genes indicative of maturation, including slow-twitch (TNNI1) and fast-twitch (TNNI2) troponins, along their respective trajectories. b, c Highly expressed genes in the undifferentiated PAX7+ stem/progenitor cell subpopulation relative to the more differentiated subpopulations. The undifferentiated cell markers included PAX7, NCAM1, MYF5, and APOE, while more differentiated cells expressed ENO3, TNNI1, TNNI2, and MYL2. The data in boxplots nested within violin plots are expressed as median, interquartile range, minimum and maximum values. d, e There was a successive increase in TNNC1 and TNNC2 expression as cells adopted higher absolute values along the trajectory, indicating a continuous differentiation process within each subpopulation. In parallel, the expression of several other genes involved in differentiation (MYOD1, MYF5) decreased, consistent with the current understanding of the cell maturation along the myogenic lineage. Shaded area indicates estimated 95% confidence intervals for the regression estimate. Normalized expression refers to log(1 + x) if not stated otherwise. SC satellite cells, ST slow-twitch, FT fast-twitch, cuff cutoff.

The monocyte cell population differentially expressed 120 genes after exercise (Fig. 3b). Ontologically, the monocytes presented a generic response to exercise, with terms such as “amide biosynthetic process” (fdr < 0.001), “protein targeting” (fdr < 0.001), and “peptide metabolic process” (fdr < 0.001) (Fig. 3c). When considering the differential expression of single genes, the monocyte population significantly expressed NFKBIA, CTSS, HLA-C, and SLC25A6 after exercise, suggesting an inflammatory response and cellular stress reactions to exercise (Fig. 3f). Lymphocytes differentially expressed 9 genes after exercise (Fig. 3b). Ontologically, the lymphocyte cells solely enriched for generic terms, suggesting an absent exercise-specific response. The genes regulating the ontological enrichment were overall generic and lacked established biological functions in the literature (e.g., FTH1, IFITM2, CALM1, and HLA-E) (Fig. 3g).

Pericytes differentially expressed 49 genes (Fig. 3b). In terms of ontological enrichment, the pericytes showed a response indicative of cellular activation, stress response, and regeneration, enriching for 38 ontologies such as “response to wounding” (fdr < 0.01), “actin mediated cell contraction” (fdr < 0.05), and “cell cycle G2 M phase transition” (fdr < 0.05) (Fig. 3c). Genes driving the regenerative response included ACTB, TPM1, TIMP3, and CD36. In terms of the greatest exercise-related response, the pericytes differentially expressed ACTB and ACTA2 after exercise (Fig. 3h). A complete list of differentially expressed genes and ontologies across cell-populations can be found in Supplementary Data 58.

Comparison of single-cell with whole-tissue sequencing in relation to exercise

We also examined whether the transcriptional responses to exercise among different cell populations using scRNA-seq were consistent with RNA-seq findings. To this end, the transcriptional responses to exercise in each cell type were compared with findings from a recent RNA-seq study22 investigating the same time-points and with a similar exercise protocol as in the current study. Of the 874 transcripts (535 unique) that were significantly regulated in ≥1 cell-type in the current scRNA-seq experiment, 187 transcripts (129 unique) were also regulated by exercise in RNA-seq study (Fig. 3i). There were no differences between cell populations in terms of coverage by RNA-seq, i.e., all cell types shared ~25% of transcripts regulated by exercise with RNA-seq.

Trajectory analysis of myogenic cells

A major advantage of scRNA-seq is the ability to use the global transcriptome of each cell to classify populations of cells of common origin into a continuum of only 1 to 2 dimensions, thereby visualizing and identifying successive, stepwise changes in gene expression from cell to cell. This technique is often referred to as trajectory analysis. Trajectory analysis has proven to be a powerful tool to deconvolute successive transcription-driven cellular processes in developmental biology and stem cell differentiation. Here, we use trajectory analysis to test whether there is evidence of a continuous transition from undifferentiated myogenic cells into increasingly mature myogenic cells (Fig. 4a). Three distinct clusters corresponding to undifferentiated PAX7+ satellite/myoblast cells, TNNI2+ fast-twitch, and TNNI1+ slow-twitch myogenic cells were selected, and two different trajectories with the undifferentiated cluster as a starting point were calculated using principal curve pseudotime analysis. Genetic markers that determined position along the first principal component included PAX7, APOE, IGFBP5, MYF5, and NCAM1 (Fig. 4b), which are canonical myogenic stem/progenitor cell markers. Among the more differentiated cells, ENO3, TNNI2, TNNT3, MYL2, TNNT1, and TNNC1 genes (Fig. 4c) drove the partitioning into distinct clusters along the second principal component. The transition of undifferentiated PAX7+ satellite/myoblast cells towards a higher degree of differentiation was proportional to expression levels of marker genes along the given trajectories. Accordingly, the expression of TNNC1 and TNNC2 increased proportionally to pseudotime, in slow- and fast-twitch myogenic cells, respectively (r2 = 0.28 and r2 = 0.32, Fig. 4d, e). A corresponding decrease in the expression of MYOD1 (r2 = −0.39 and r2 = 0.13) and MYF6 (r2 = −0.29 and r2 = −0.22) was observed along the trajectories for both fast- and slow-twitch cells (Fig. 4d, e).

Furthermore, we examined the effect of exercise on the transition from PAX7+ undifferentiated satellite/myoblast cells to fast- and slow-twitch expressing cells and the overall transcriptional effect of exercise in these subpopulations. Three hours after a single bout of exercise, there was a small (Δslow-twitch = 5.4%, p < 0.001; Δfast-twitch = 9.0%, p < 0.001) but statistically significant incremental shift in pseudotime toward a higher degree of differentiation in both the slow- and fast-twitch cell populations (Fig. 5a, b). Apart from the effects on pseudotime, there was a common transcriptional response to a single bout of exercise for 24 genes in all three myogenic subpopulations. These genes included mostly mitochondrial and ribosomal genes. More genes were regulated by exercise in the undifferentiated PAX7+ (255 genes) and slow-twitch (138 genes) subpopulations, compared to the fast-twitch subpopulation (51 genes) (Fig. 5c). Genes regulated by exercise in the undifferentiated PAX7+ myogenic cells included NEAT1, NNMT, CXXC5, MT2A, and SQSTM1 (Fig. 5d). The slow-twitch TNNI1+ cells responded to exercise by regulating genes involved in “muscle organ development” (fdr < 0.001) including MYLPF, ACTA1, MYL2, and MYH7 (Fig. 5d). In the fast-twitch TNNI2+ cells, the exercise response included upregulation of changes in MYBPC1, MYBCP2 (“muscle contraction”, fdr < 0.001), TXNIP, UBC, and OPTN (Fig. 5d). Gene-ontology enrichment in the myogenic cells following exercise can be found in Supplementary Data 9.

Fig. 5: Exercise effects on myogenic cells.
figure 5

The effect of exercise on the myogenic cell populations fate was further investigated: a, b Three hours post-exercise there was a significant change along the trajectories with a shift of 9.0 and 5.4% (p < 0.001 for both) in fast- and slow-twitch myogenic cells respectively, indicating increased differentiation. Boxplots and ECDFs denote the position along pseudotime of the myogenic cells pre- vs. post-exercise. c Venn diagram of exercise-regulated genes in the myogenic subpopulations. The larger exercise effect was observed among the undifferentiated PAX7+ satellite/myoblast subpopulation compared to the more mature subpopulations. A substantial portion of the exercise-regulated genes in the more mature fast- and slow-twitch subpopulations were also regulated in the undifferentiated PAX7+ subpopulation. d Scatter/volcano plot presenting the transcriptional effects of exercise for each myogenic subpopulation. Differentially expressed genes (fdr < 0.05) are highlighted where distinct color denotes respective subpopulation. e In vitro validation experiments through analysis of gene expression of slow- and fast-twitch troponins in primary human myoblasts undergoing differentiation. Boxplots depict gene-expression of TNNC2 and TNNC1 mRNA levels assessed through RT-PCR at baseline, after 4 and 9 days of differentiation towards myotubes. Statistical analysis was conducted through One-way ANOVA with the Tukey test as a post hoc. f C2C12 cells in proliferation versus differentiation media with gene expression analyzed using microarrays obtained through LIMMA. Where present the data in boxplots are expressed as median, interquartile range, minimum and maximum values, and individual points are shown as black dots. Normalized expression refers to log2(x) if not stated otherwise. SC satellite cells.

In vitro validation of myogenic subpopulations

The observation of a regulated expression of contractile elements such as TNNC1 and TNNC2 in myogenic cells, and that such transcriptional upregulation is associated with the initiation of differentiation towards a more mature myofiber-like phenotype was validated in vitro. Primary myoblasts were isolated from human muscle biopsy and kept in proliferation media until confluence. Differentiation towards myotube formation was initiated according to standard protocols and the expression of key-marker genes from the myoblast subpopulations identified as undergoing differentiation were analyzed with qPCR on day 0, day 4, and day 9 of the differentiation process (Fig. 5e). Slow-twitch troponin (TNNC1) increased from 9.3 ± 2 a.u on day 0 to 331 ± 150 after 4 days of differentiation (p < 0.001). It remained elevated after 9 days of differentiation (117 ± 20) (p < 0.001). Gene-expression of fast-twitch troponin (TNNC2) was 2.2 ± 1 a.u on day 0, increased to 123 ± 10 a.u after 4 days of differentiation (p < 0.001), and remained elevated after 9 days of differentiation (155 ± 30) (p < 0.001).

Finally, we utilized a publicly available microarray experiment conducted in a mouse myoblast cell-line (C2C12-cells) evaluation gene-expression in cells undergoing differentiation (n = 3) in relation to cells in proliferation-media (n = 3) (Fig. 5f). TNNC1 and TNNC2 were elevated with a log2FC of 5.7 and 7.7 respectively (fdr < 0.001) in differentiating versus proliferating C2C12-cells.

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