Revealing the Molecular Signature of Early Mesodermal Lineages by Single Cell Transcriptomics
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 the key genes that regulate the specification of mesodermal lineages. 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 mechanism that govern mesodermal lineage differentiation.
Using Nkx2-5- and Etv2-EYFP transgenic embryos, from which progenitors of cardiovascular/circulatory lineages can be isolated with high purity, these researchers performed single cell transcriptome analysis. The single cell RNA sequencing is a new exciting technique that allows profiling of the expression profiles at the single cell level. However, due to the heterogeneity of the single cell RNA-seq profiles and high technical noise (especially the dropout noise), accurately discovering the hidden cell population and reconstructing the complex lineage hierarchies from the single cell RNA-seq data became a challenging task in this field. To address the dropout noise, and reconstruct the differentiation pathways, the researchers developed a series of novel concepts and computational methods such as weighted Poisson non-negative matrix factorization (wp-NMF), random walk on a heterogeneous metagene-metacell graph, metacell entropy and a unified statistical framework called topographic cell map (TCM) to discover the hidden cell populations, separating the progenitor from the committed cellular states and rebuild the hierarchies of mesodermal lineage specifications. The results were very successful. This project focused on the analysis of complete gene expression profiles during mesodermal lineage specification at a single cell resolution. These results demonstrated the power of combining machine learning algorithms and single cell RNA-seq for discovering hidden cell types, novel lineage markers and reconstructing lineage hierarchies.
In 2016, the lab plans to generate the single cell expression profiles of other important mesodermal genes such as T (Brachyury), Kdr, Gata1, Isl1, and others. They aim to improve their current single cell RNA-seq pipeline, and develop new computational methods for discovering various differentiation pathways, predicting key genes controlling for lineage switching, and integrating multiple dimensional genomic data at the single cell level. This entire project should generate a full blueprint of lineage hierarchies during early embryonic development and provides insights into related biological mechanisms, disease process and designing therapeutic strategies.
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