Abstract 18849: Molecular Signature of Early Cardiovascular Lineages Revealed by Single Cell Transcriptomics
Background: The molecular definition of the differentiation process of cardiovascular lineages is of intense interest for developmental biologists. Recent studies have demonstrated that multipotent cardiac progenitors are capable of giving rise to multiple cardiovascular lineages such as endothelium, endocardium, epicardium and myocardium. Moreover, Nkx2-5 and Etv2 are two of the key genes that regulate the specification of the cardiac lineage. Understanding the global gene expression pattern and ontogeny of Nkx2-5 and Etv2 positive cells are critical steps for the decoding of the signaling cascade and the molecular mechanisms that govern mesodermal lineage differentiation.
Methods: Using Etv2- and Nkx2-5-EYFP transgenic embryos, from which progenitors of cardiovascular/circulatory lineages can be isolated with high purity, we performed single cell transcriptome analysis. We used our recently developed single cell RNA-seq analysis pipeline, dpath, to decompose the transcriptome into seven major metagenes. Each gene and each cell was assigned the “metagene signature” based on its unique and robust features, which successfully captured their molecular dynamics and global expression patterns.
Results: An unbiased clustering analysis using the metagene signature of 459 Etv2- and Nkx2-5 EYFP positive cells from E7.75 and E8.25 embryos revealed 16 mesodermal cell populations with distinct global gene expression patterns. The metagene entropy analysis revealed distinct differentiation potential and mesodermal lineage coverage among Etv2-EYFP+ and Nkx2-5-EYFP+ cells. Two major Nkx2-5+ cell populations were significantly associated with the first and second heart fields and novel gene markers for each heart fields were identified. We also discovered multiple types of endocardial populations from different sources and with distinct global gene expression patterns.
Conclusions: We report the analysis of complete gene expression profiles during mesodermal lineage specification at a single cell resolution. These results demonstrate the power of combining machine learning algorithms and single cell RNA-seq for discovering hidden cell types from mixed cell population and novel lineage markers.
Author Disclosures: W. Gong: None. S. Das: None. W. Pan: None. N. Koyano-Nakagawa: None. D.J. Garry: None.
- © 2015 by American Heart Association, Inc.