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Review
. 2018 Feb 13;5(4):539-548.
doi: 10.1016/j.jcmgh.2018.01.023. eCollection 2018.

Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems

Charles A Herring  1   2 , Bob Chen  1   3 , Eliot T McKinley  1   4 , Ken S Lau  1   2   3
Affiliations
Review

Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems

Charles A Herring et al. Cell Mol Gastroenterol Hepatol. .

Abstract

Function at the organ level manifests itself from a heterogeneous collection of cell types. Cellular heterogeneity emerges from developmental processes by which multipotent progenitor cells make fate decisions and transition to specific cell types through intermediate cell states. Although genetic experimental strategies such as lineage tracing have provided insights into cell lineages, recent developments in single-cell technologies have greatly increased our ability to interrogate distinct cell types, as well as transitional cell states in tissue systems. From single-cell data that describe these intermediate cell states, computational tools have been developed to reconstruct cell-state transition trajectories that model cell developmental processes. These algorithms, although powerful, are still in their infancy, and attention must be paid to their strengths and weaknesses when they are used. Here, we review some of these tools, also referred to as pseudotemporal ordering algorithms, and their associated assumptions and caveats. We hope to provide a rational and generalizable workflow for single-cell trajectory analysis that is intuitive for experimental biologists.

Keywords: Cell State Transition; Differentiation; MST, minimum spanning tree; PCA, principal component analysis; Pseudotime; Single-Cell Analysis; Stem Cells; Trajectory; scRNA-seq, single-cell RNA-sequencing; t-SNE, t-distributed stochastic neighbor embedding.

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Figures

Figure 1
Figure 1
General workflow of trajectory analysis algorithms. Beginning with data in multidimensional space, feature selection is first performed to include relevant analytes and exclude noise. From the selected feature set, dimension reduction is applied to best emphasize the part of the data most relevant to cell-state transitions. Trajectories then are reconstructed in this reduced space and analyzed as pseudotime courses.
Figure 2
Figure 2
New approaches for trajectory analysis from single-cell data. (A) Monocle2 embeds the data cloud into a graph composed of principal curves. (B) p-Creode learns the most likely path through the data cloud as a function of density and shape. Arrows represent data embedding into the graph.
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