Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate.
Researchers from Memorial Sloan Kettering Cancer Center present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. The researchers apply their algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. The researchers show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
Palantir characterizes cell fate choices in a continuous model of differentiation
a, Top: Projection of CD34+ human bone marrow cells along diffusion components. Bottom: Expression of gene pairs involved in lineage decisions for cells in the corresponding top panel. Cells colored by Phenograph cluster (Supplementary Fig. 4a); arrows highlight continuity in cell fate choices as a pervasive lack of well-defined branch points in decision-making regions. Plots show comparison of 3,170, 4,224, and 3,510 cells, respectively. b–d, Palantir phenotypic manifold for a subsampled dataset of CD34+ human hematopoiesis. Each dot represents a cell embedded into diffusion space based on the first three components and visualized using tSNE. b, Cartoon of Markov chain construction over the manifold. Cells colored by pseudo-time. c, Cells colored by the stationary distribution of the Markov chain in b, demonstrating outliers (yellow) in the mature states. Outliers that are also boundary states (circles) are selected as terminal states. d, Cells colored by differentiation potential (DP). Highlighted examples (circles) show relationship between pseudo-time, DP, and branch probabilities (histogram with bars colored by terminal state or branch, Br). High DP (1) decreases gradually as cells move toward commitment (2 and 3). Modeling cell fate choices as probabilities provides a representation of their continuity (4–7). e, Expression of a branch A-specific gene along pseudo-time. Left: Each dot represents a cell colored by its probability of reaching terminus A. Black line, gene expression trend for this data. Right. Expression trends for the three lineages. The unified framework of pseudo-time and branch probabilities enables gene expression dynamics to be characterized across a common axis. DC, dendritic cells; Ery, erythroid cells; Mega, megakaryocytes; Mono, monocytes; Myl, myeloid cells.
Availability – Palantir is available as a Python module at https://github.com/dpeerlab/Palantir/.