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Single-Cell Transcriptomics Studies Draw Cell Type Maps for Complete Animal

NEW YORK (GenomeWeb) - Two new studies tout the use of single-cell transcriptome profiling to draw cell type atlases and establish cell lineages in a whole complex animal.

Both papers, published yesterday in Science, used Drop-seq, a high-throughput transcriptome-profiling platform developed at Harvard University and the Broad Institute, to characterize all cell types in the flatworm Schmidtea mediterranea, a model organism.

Researchers from the Whitehead Institute for Biomedical Research and the Massachusetts Institute of Technology, led by senior author Peter Reddien, reported that they were able to determine the transcriptomes for essentially every cell type of S. mediterranea. They sequenced more than 50,000 single cells and identified at least 44 distinct major cell clusters, as well as more than 150 subclusters.

"Much like the genome of an animal, we propose this atlas-like dataset of cell-type transcriptomes can serve as a resource fueling an immense amount of research, not only in planarians, but in other bilaterians with similar cell types," they wrote.

In the other paper, researchers in Germany, led by senior author Nikolaus Rajewsky from Berlin’s Max-Delbrück Center for Molecular Medicine, also reported creating a cell type atlas for S. mediterranea, as well as a lineage tree. They sequenced more than 20,000 individual cells and identified at least 51 cell clusters and 23 independent cell lineages.

"Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution," the researchers wrote, adding that the approach "will become an indispensable method" for their field.

S. mediterranea is a planarian, a type of flatworm, that has the ability to regenerate any body part as an adult. "Because of the constant turnover of planarian tissues, essentially all stages of all cell lineages from pluripotent stem cell to differentiated cell are anticipated to be present in the adult," the Whitehead team wrote. Thus, analyzing the whole organism would provide the respective groups with a near-complete picture of the available cell types.

The German team said they validated several recently reported rare cell types and discovered several more, while the Whitehead team suggested it had "essentially reached saturation for determining the cell type transcriptomes of asexual planarians."  

For cell type cluster analysis, both groups used a computational approach called Seurat that derives spatial relationships between individual cells using RNA-seq data, and they are posting their data and tools online. In particular, the Whitehead team developed a tool to generate cluster expression data and the German team created an interactive portal for accessing their single-cell data.

The German team contrasted their approach to high-throughput lineage tracing methods using transgenic or CRISPR-based perturbations, and noted that "it can be applied to every species provided that single cells can be isolated and sequenced." They added that the approach could also identify de novo stem cells and their differentiation trajectories and discover sets of genes involved in differentiation in other species — a topic for future studies.