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

Abstract

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 markers1-3 and the effects of ensemble averaging in conventional transcriptome analysis experiments on cell populations1–5. 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.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

“…Other reports underscore the role of cell‐to‐cell gene expression variability in cell commitment in different hematopoietic progenitor cells, resulting in the independent activation of regulator genes in the absence of a coordinated lineage program 36, 37, which suggests that cell fate commitment can occur through multiple alternative pathways. Similar results have been obtained in other systems, such as the study of murine lung development, in which single cell transcriptomics data revealed cell‐type specific transcriptional regulators that discriminate between different populations that define the cellular hierarchy of the distal mouse lung epithelium 38. Single‐cell studies of the sub‐regions of the embryo, have also provided key insights of the initial phases of multicellular organisms development, allowing the identification of regulators triggering segregation between cell populations in early mouse embryos 39, and the delineation of gene regulatory mechanisms underlying progressive development of early mammalian embryos 40, 41.…”

Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

“…We performed principal component analysis on all 197 single-neuron transcriptomes as previously reported [32]. Genes with the highest loading in the first three principal components were analyzed by unsupervised hierarchical clustering.…”

“…By single-cell PCR, Chiu et al identified six subgroups of DRG neurons [25]. Single-cell RNA-seq enables a better understanding of a cell's transcriptome [26][27][28][29][30][31][32][33]. Usoskin et al performed low-coverage single-cell RNA-seq (3 574 ± 2 010 genes per neuron) and classified the mouse DRG neurons into two PEP types, three NP types, TH type and five NF200-positive types within the traditional classification framework [34].…”

Single‐cell technologies to study the immune system

“…2)23, 24, but the discrimination between cell state and cell type still needs to be further validated experimentally. In other words, the distinction between physiological fluctuations of gene expressions without phenotypic changes, and different cells types cannot be made solely by analysis of gene expression pattern.…”