A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development

Abstract

Summary During human brain development, multiple signaling pathways generate diverse cell types with varied regional identities. Here, we integrate single-cell RNA sequencing and clonal analyses to reveal lineage trees and molecular signals underlying early forebrain and mid/hindbrain cell differentiation from human embryonic stem cells (hESCs). Clustering single cell transcriptomic data identified 41 distinct populations of progenitors, neuronal, and non-neural cells across our differentiation time course. Comparisons with primary mouse and human gene expression data demonstrated rostral and caudal progenitor and neuronal identities from early brain development. Bayesian analyses inferred a unified cell type lineage tree that bifurcates between cortical and mid/hindbrain cell types. Two methods of clonal analyses confirmed these findings and further revealed the importance of Wnt/beta-catenin signaling in controlling this lineage decision. Together, these findings provide a rich transcriptome-based lineage map for studying human brain development and modeling developmental disorders.

Single-Cell Profiling of an In Vitro Model of Human Interneuron Development Reveals Temporal Dynamics of Cell Type Production and Maturation

“…We constructed an hESC line with Citrine (Cit) fused to the endogenous copy of the DCX gene in the H1 parent line to allow us to profile neurons (Cit + ) and progenitors (Cit − ) (Yao, 2017). A majority of cells were FOXG1 (76.2 ± 2.9%) and NKX2-1 + (85.7± 0.3%) by day 24 ( Figure 1J, N), indicating that the DCX-citrine line differentiated comparably to the parent line ( Figure 1N; compare with Figure 1F).…”

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

“…To date, many studies have shown the heterogeneity of neural precursor cells (Johnson et al 2015;Llorens-Bobadilla et al 2015) and neurons (Molyneaux et al 2007;Pollen et al 2014;Darmanis et al 2015;Usoskin et al 2015) in mouse and human brain by scRNA-seq. However, due to complexity of data analysis of cellular dynamics, coupled with the biological variability (birth, death, and differentiation) of individual cells, as well as the presence of technical, environmental, and intracellular noise (Kuznetsov 2001(Kuznetsov , 2003Kuznetsov et al 2002;Kim and Marioni 2013;Kharchenko et al 2014;Buettner et al 2015;Daigle et al 2015;Vu et al 2016), it remains a challenge to interpret the heterogeneity and dynamics of NPC to neuron transitions (Camp et al 2015;Bakken et al 2016;Yao et al 2017). Given the lack of synchronous development, the molecular patterns that switch on and switch off pathways governing alternative neuronal fate choices (Ming and Song 2011) are not clear.…”

Discovering sparse transcription factor codes for cell states and state transitions during development

“…‘0’ refers to the null topology. Triplets with p>0.6 were used to construct the lineage tree. DOI: http://dx.doi.org/10.7554/eLife.20488.02110.7554/eLife.20488.022Probabilities of Transition and Marker Genes for the Human Brain Developmental Lineage Tree.Listed are, for crucial triplets along the lineage tree, the genes with the p>0.8 of belonging to the two transition gene classes and the root marker class, and their associated probabilities. DOI: http://dx.doi.org/10.7554/eLife.20488.02210.7554/eLife.20488.023Human Brain Development SmartSeq2 Census.Further detail can be found Materials and methods and in Yao et al (2017) (Supplemental Information). DOI: http://dx.doi.org/10.7554/eLife.20488.02310.7554/eLife.20488.024List of Human Transcription Factors.List of human transcription factors used to cluster and infer lineages for the cortical differentiation tree. List was adapted from (Ben-Porath et al, 2008).…”