• Sample Source: Mouse cardiac outflow tract
• Target Cell Count: 64,605 cells, divided into 3 groups (ps47, ps49, ps51), each representing different developmental stages (early, middle, late), with two biological replicates for each stage.

• Clustering:
  • Perform clustering analysis on the sequencing data to identify distinct cell populations.
• K-Nearest Neighbors (KNN) Graph Layout:
  • Use a KNN graph to visualize the relationships between cells.
• RNA Velocity Analysis:
  • Assess the rate of gene expression changes to infer the direction of cell differentiation.
• Differential Expression Analysis:
  • Compare gene expression levels between different cell populations or stages to identify significant differences.
• Pseudo-Temporal Ordering:
  • Order cells along a pseudo-time trajectory based on gene expression patterns to simulate the developmental path.
• Gene Regulatory Network (GRN) Analysis:
  • Construct gene regulatory networks to identify key regulatory factors and their relationships.

• Single-Molecule Fluorescence In Situ Hybridization (smFISH):
  • Use fluorescent probes to detect the expression location and levels of specific genes, validating the results from RNA sequencing.
• Lineage Tracing:
  • Trace the paths of cell differentiation to validate the developmental trajectories and gene regulatory relationships.

Sequencing Platform:

• The sequencing was performed using the Illumina NovaSeq 6000 system, which is designed for high-throughput sequencing.

Results

1. Unsupervised clustering identified 17 cell clusters that represent distinct cell types or cell subpopulations.The relative proportions varied greatly between stages, as reflected by the observation that the cell fractions for some clusters, e.g., c11, remarkably changed during development.
2. The six lineages identified by established markers were further confirmed by hierarchical clustering, which showed that the clusters assigned to the same lineage were grouped together and closely aligned on the tree. The mesenchymal lineage constituted the most abundant cell type (46.3%), and the relative proportion significantly decreased at the late stage (Student’s t test, p < 0.05), suggesting that an active cellular transition occurred. Whereas the VSMC lineage expanded during development, in accordance with the myocardial-to-arterial phenotypic change.
3. The expression profile analysis supports that c9 and c4 cells are in an intermediate state along the trajectory for myocardium-to-VSMC trans-differentiation. Compared with c2, c9 showed significant upregulation of VSMC markers, including contractile VSMC markers and synthetic VSMC markers.

Conclusion

In conclusion, through large-scale single-cell transcriptomic sequencing, they identify convergent development of the vascular smooth muscle cell (VSMC) lineage, with these cells arising either by a myocardial-to- VSMC trans-differentiation or mesenchymal-to-VSMC transition.

Reference

Liu, Xuanyu, et al. “Single-cell RNA-seq of the developing cardiac outflow tract reveals convergent development of the vascular smooth muscle cells.”  Cell Reports 28.5 (2019): 1346-1361.