A dynamical systems treatment of transcriptomic trajectories in hematopoiesis.
Simon L FreedmanBingxian XuSidhartha GoyalMadhav ManiPublished in: Development (Cambridge, England) (2023)
Inspired by Waddington's illustration of an epigenetic landscape, cell-fate transitions have been envisioned as bifurcating dynamical systems, wherein exogenous signaling dynamics couple to a cell's enormously complex signaling and transcriptional machinery, to elicit qualitative transitions in the cell's collective state. Single-cell RNA sequencing (scRNA-seq), which measures the distributions of possible transcriptional states in large populations of differentiating cells, provides an alternate view, in which development is marked by the variations of a myriad of genes. Here, we present a mathematical formalism for rigorously evaluating, from a dynamical systems perspective, whether scRNA-seq trajectories display statistical signatures consistent with bifurcations and, as a case study, pinpoint regions of multistability along the neutrophil branch of hematopoeitic differentiation. Additionally, we leverage the geometric features of linear instability to identify the low-dimensional phase plane in gene expression space within which the multistability unfolds, highlighting novel genetic players crucial for neutrophil differentiation. Broadly, we show that a dynamical systems treatment of scRNA-seq data provides mechanistic insights into the high-dimensional processes of cellular differentiation, taking a step toward systematic construction of mathematical models for transcriptomic dynamics.
Keyphrases
- single cell
- rna seq
- gene expression
- high throughput
- genome wide
- density functional theory
- dna methylation
- depressive symptoms
- cell fate
- oxidative stress
- induced apoptosis
- cell death
- magnetic resonance imaging
- cell cycle arrest
- machine learning
- signaling pathway
- copy number
- combination therapy
- heat shock
- cell proliferation