Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape

Abstract

We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.single cell | gene expression | bifurcation | differentiation

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice

“…Analysis of the data using six independent computational approaches 1,3,4,16,17 resulted in varied delineation of cellular states and intermediates (Supplementary Information, Extended Data Fig. 1-5).…”

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

“…To date, several computational methods have been reported that profile developmental processes, such as Monocle (Trapnell et al 2014), Wanderlust (Bendall et al 2014), Wishbone (Setty et al 2016), SLICER (Welch et al 2016), Diffusion Pseudotime , Destiny (Angerer et al 2016), and SCUBA (Marco et al 2014). These methods attempt to order cells into smooth continuous spatiotemporal trajectories to model development.…”

Current Challenges in Cell-Type Discovery Through Single-Cell Data

“…Dimensionality reduction techniques such as principal component analysis and t-distributed Stochastic Neighbor Embedding (t-SNE) shift the interpretation of nonlinearities and cell population clusters to visual inspection of distance-preserving projections of the high dimensional single cell data (5). Besides a first recent systems theory-based attempt to detect bifurcation events from single cell transcriptomics data of developmental processes (10), no constructive and robust approach has been able to objectively describe nonlinear geometries and trajectories for heterogeneous cell populations. Cellular differentiation, in particular, hematopoietic differentiation can follow a nonlinear bifurcated topology (11), where hematopoietic stem cells at the root give rise to a multitude of cellular types through division and differentiation following a branching pattern.…”