Single cell transcriptomic analyses of muscle cell

The data described in the previous paragraph suggest that Sca1-expressing cells possess a certain gene expression identity common to both satellite cells and FAPs. FAPs are positive for PDGFRalpha⁶³ but also express SCA1⁶⁴. This could possibly explain the similarity of the transcriptomic profile between Sca1-expressing cells and FAPs. Satellite cells are commonly defined as a population characterized by the expression of the transcriptional factor Pax7 although they represent a heterogeneous population composed of cells with different biochemical and functional features¹⁵⁶. Due to this heterogeneity and the similarity found between satellite cells and Sca1-expressing cells at the gene expression level upon cardiotoxin injection, it was speculated whether a certain number of satellite cells could possibly express Sca1 and this expression could mark a specific subpopulation. In order to investigate the cellular heterogeneity of satellite cells and the possible different characteristics of the Sca1⁺ and Sca1^- populations, the same markers used for the microarray analyses (section 6.2.6) were used as well as SM/C2.6 for sorting as the equivalent marker for satellite cells¹⁵³ instead of Pax7. The following six populations were isolated (Figure 32a):

 FAPS : PDGFRalpha⁺SCA1^- & PDGFRalpha⁺SCA1⁺,

All the populations were negatively gated for the expression of CD31 and CD45. Single cells were sorted from each population. The transcription level of Sca1 (Ly6a), Pdgfralpha and Pax7 genes was tested using RT-qPCR for the single cells of the six populations mentioned above to examine the accuracy of the sorting. Only the single cells expressing the markers specific for the population they belong to, were chosen for single cell gene expression analyses. The single cell gene data profile of the samples was generated using Fluidigm Dynamic Arrays technology (BioMarkTM HD System). This technology allows performing several distinct single RT-qPCR reactions together, at the same time, using a specific microarray chip card.

From the six different populations, the number of single cells able to pass the quality test and suitable for the next analyses resulted to be 182 and was divided as follows: SM-C2.6⁺SCA1⁺CD31^-CD45^- (32 cells), SM-C2.6⁺SCA1^-CD31^-CD45^- (32 cells), PDGFRalpha⁺SCA1⁺CD31^-CD45^- (31 cells), PDGFRalpha⁺SCA1^-CD31^-CD45^- (23 cells).

Two samples were created using all the cells mentioned above together with C2C12 and ES cells as internal positive controls (Figure 32b). The samples were analyzed for the expression of 288 genes specific for cell signaling (e.g. BMP, Notch, Wnt), cell cycle, chemokines, growth factors and stem cell markers. For each sample four different microarrays were performed using the primers mentioned above. In order to be able to further analyze the microarray data obtained, the beta-actin expression value of the four arrays were compared using Pearson correlation. Pearson correlation defines how well different data sets are related and the linear relationship existing between them. The Pearson coefficient range is defined to be from −1 to 1, where 1 represents a positive linear correlation between the values. As shown in Figure 32c, the comparison of the data generated by Fludigm technology presented a Pearson coefficient close to 1, confirming that the data obtained from the four arrays had high correlation and allowing further analyses.

Figure 32. Transcriptomic analyses of different populations of muscle cells.

(a)Six different populations of cells were isolated from the skeletal muscle: SCA1⁺PDGFRalpha^-; SCA1⁺PDGFRalpha⁺; PDGFRalpha⁺SCA1^-; SCA1⁺SM/C2.6^-; SCA1⁺SM/C2.6⁺; SM/C2.6⁺SCA1^-. Representative images of dot plots and FACS gates used to isolate the six populations. (b) Representation of single cells of each population plated in 96-well plates. Two samples were created. The first sample contains the following cell types: in column number one the positive controls, consisting of 4 wells of 100 C2C12 cells and 4 wells of 100 ES cells; 32 cells of the SM/C2.6⁺SCA1^- population (satellite cells-Sca1^-); 32 cells of the SM/C2.6⁺SCA1⁺ population (satellite cells-Sca1⁺); 23 cells of the PDGFRalpha⁺SCA1^- population (FAPs-Sca1^-).

In the second plate: 32 cells of the SM/C2.6^-SCA1⁺ population (Sca1⁺cells- SM/C2.6^-); 32 cells of the PDGFRalpha^-SCA1⁺ population (Sca1⁺cells-PDGFRalpha^-); and 31 cells of the PDGFRalpha⁺SCA1⁺ (FAPs-Sca1⁺) population. In both sample plates, the last well was left empty (without sample) as an internal negative

Based on the Fluidigm data, a heatmap was created to visualize the expression variance of the genes in single cells belonging to FAPs cells (PDGFRalpha⁺Sca1^- and PDGFRalpha⁺Sca1⁺populations) and satellite cells (SM/C2.6⁺Sca1^- and SM/C2.6⁺Sca1⁺ populations) (Figure 33a & 34a). The results were then clustered using the R package clValid (as described before in section 5.9.2), in order to group cells presenting the same gene expression (Figure 33b & 34b). However, also after the clustering it was not possible to visualize a common unique gene profile for FAPs and satellite single cells. Although the cells were sorted for the same markers, at a single cell level FAPs and satellite cells reveal a high gene expression variance.

The clustering did not identify any difference in FAPs and satellite cells populations in the presence or the absence of Sca1. In order to reveal some distinct features, Student t-Test was performed in satellite cells (SM/C2.6⁺Sca1^- population versus SM/C2.6⁺Sca1⁺ population) and FAPS (PDGFRalpha⁺Sca1^- population versus PDGFRalpha⁺Sca1⁺ population). Based on the t-Test significance, in absence or presence of Sca1 some genes resulted to be significant (Figure 33c & 34c).

In FAPs not expressing Sca1 (PDGFRalpha⁺Sca1^-), the following genes were highly expressed:

 Bmp1 (Bone morphogenic protein 1),

 Hey2 (Hairy/enhancer-of-split related with YRPW motif protein 2, also known as cardiovascular helix-loop-helix factor 1 (CHF1)),

 Pdgfbeta (Platelet-derived growth factor subunit beta),

 Vegfc (Vascular endothelial growth factor C).

The subpopulation of FAPs expressing Sca1 (PDGFRalpha⁺Sca1⁺) was in contrast characterized by the expression of:

 Lmna (Lamin A/C),

 Il6st (Interleukin 6 signal transducer or gp130, or Oncostatin M receptor),

 Stat6 (Signal transducer and activator of transcription 6),

 Bmp4 (Bone morphogenic protein 4).

Satellite cells (SM/C2.6⁺SCA1^- versus SM/C2.6⁺SCA1⁺ population) instead of FAPs present a different trend. In the subpopulation of satellite cells expressing Sca1 (SM/C2.6⁺SCA1⁺) the following genes were higher expressed (Figure 34c):

 Bmp1 (Bone morphogenic protein 1),

 Pax7 (Paired box gene 7),

 Eltd1 (EGF, Latrophilin seven transmembrane domain containing 1).

In the satellite cell fraction not expressing Sca1 (SM/C2.6⁺SCA1^-), other genes were highly expressed:

 Il6st,

 Hes1 (Hairy and enhancer of split 1 (Drosophila)),

 Kit (c-kit, CD117, belly spot, steel factor receptor),

 Numbl (Numb-like).

Interestingly, as already observed in the microarray data for the Sca1-expressing cell population, also in satellite cells respectively in the presence and absence of Sca1, the expression of Bmp1 and Il6 was detected. It has been speculated that the presence of Bmp1 in Sca1-expressing cells is involved in maintaining their quiescence, whereas the Il6 expression promote their myogenic commitment. In this regard the presence of Bmp1 in SM/C2.6⁺SCA1⁺ population could mark a quiescent subpopulation, probably the actual cells able to replenish the stem cell pool after damage. On the other hand, the high level of Il6 in satellite cells not expressing Sca1 could reflect their tendency to be more prone to differentiation.

These data demonstrate that at a single cell level it is possible to observe a high heterogeneity in both satellite cells and FAPs population. Furthermore, the presence of Sca1 in satellite cells seems to mark a subpopulation with higher quiescent characteristics.

Figure 33. Profiling of fibro-adipocyte progenitor cells (FAPs).

PDGFRalpha^-SCA1⁺ and PDGFRalpha⁺SCA1^- were compared. (a) Representative image of a heat map highlighting the genes expressed in single cells from PDGFRalpha⁺Sca1^- and PDGFRalpha⁺Sca1⁺populations.

The gene profile data were obtained by Fluidigm single cell RT-qPCR. In the heat map the yellow spots indicate a high expression and the blue ones a low expression. (b) A result of hierarchical clustering performed by Rpackage clValid in order to group cells presenting the same gene expression in FAPs population. (c) Representative graphs of genes significantly up or downregulated in the population not expressing (grey bar) or expressing Sca1 (white bar). After performing Student t-Test between PDGFRalpha^-SCA1⁺ and PDGFRalpha⁺SCA1^- the following genes resulted to berelevant: Bmp1, Hey2, Pdgfd, Vegfc, Lmna, Il6st, Stat6, Bmp4.

Figure 34. Profiling of satellite cells.

SM/C2.6^-SCA1⁺ and SM/C.6⁺SCA1^- were compared. (a) Representative image of a heat map highlighting the genes expressed in single cells of SM/C2.6^-SCA1⁺ and SM/C.6⁺SCA1^- populations. The gene profile data were obtained by Fluidigm single cell RT-qPCR. In the heat map the yellow spotsr indicate a high expression and the blue ones a low expression. (b) A result of hierarchical clustering performed by Rpackage clValid in order to group cells presenting the same gene expression in satellite cells population. (c) Representative graphs of genes significant highly up or down regulated in the population not expressing (grey bar) or expressing Sca1 (white bar). After performing Student t-Test between SM/C2.6^-SCA1⁺ and SM/C.6⁺SCA1^- the following genes resulted