Publications

Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq

Treutlein, B., Brownfield, D.G., Wu, A.R. et al.

The mammalian lung is a highly branched network, in which the distal regions of the bronchial tree transform during development into a densely packed honeycomb of alveolar air sacs that mediate gas exchange. Although this transformation has been studied by marker expression analysis and fate-mapping, the mechanisms that control the progression of lung progenitors along distinct lineages into mature alveolar cell types remain obscure, in part due to the limited number of lineage markers and the effects of ensemble averaging in conventional transcriptome analysis experiments on cell populations. We used microfluidic single cell RNA sequencing (RNA-seq) on 198 individual cells at 4 different stages encompassing alveolar differentiation to measure the transcriptional states which define the developmental and cellular hierarchy of the distal mouse lung epithelium. We empirically classified cells into distinct groups using an unbiased genome-wide approach that did not require a priori knowledge of the underlying cell types or prior purification of cell populations. The results confirmed the basic outlines of the classical model of epithelial cell type diversity in the distal lung and led to the discovery of many novel cell type markers and transcriptional regulators that discriminate between the different populations. We reconstructed the molecular steps during maturation of bipotential progenitors along both alveolar lineages and elucidated the full lifecycle of the alveolar type 2 cell lineage. This single cell genomics approach is applicable to any developing or mature tissue to robustly delineate molecularly distinct cell types, define progenitors and lineage hierarchies, and identify lineage-specific regulatory factors.

Citation

Treutlein, B., Brownfield, D.G., Wu, A.R. et al. "Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq" Nature (2014): 371–5