Immobilized NRG1 Accelerates Neural Crest like Cell Differentiation Toward Functional Schwann Cells Through Sustained Erk1/2 Activation and YAP/TAZ Nuclear Translocation.

Neural Crest cells (NC) are a multipotent cell population that give rise to a multitude of cell types including Schwann cells (SC) in the peripheral nervous system (PNS). Immature SC interact with neuronal axons via the neuregulin 1 (NRG1) ligand present on the neuronal surface and ultimately form the myelin sheath. Multiple attempts to derive functional SC from pluripotent stem cells have met challenges with respect to expression of mature markers and axonal sorting.

Single-cell transcriptional uncertainty landscape of cell differentiation.

Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model.

Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.

When human cord blood-derived CD34+ cells are induced to differentiate, they undergo rapid and dynamic morphological and molecular transformations that are critical for fate commitment. In particular, the cells pass through a transitory phase known as "multilineage-primed" state. These cells are characterized by a mixed gene expression profile, different in each cell, with the coexpression of many genes characteristic for concurrent cell lineages.

CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis.

We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events.

Constraints on Human CD34+ Cell Fate due to Lentiviral Vectors Can Be Relieved by Valproic Acid.

The initial stages following the cytokine stimulation of human cord blood CD34+ cells overlap with the period when lentiviral gene transfer is typically performed. Single-cell transcriptional profiling and time-lapse microscopy were used to investigate how the vector-cell crosstalk impacts on the fate decision process. The single-cell transcription profiles were analyzed using a new algorithm, and it is shown that lentiviral transduction during the early stages of stimulation modifies the dynamics of the fate choice process of the CD34+ cells.

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles.

Motivation: Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process.

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging.