Creating Lineage Trajectory Maps Via Integration of Single-Cell RNA-Sequencing and Lineage Tracing: Integrating transgenic lineage tracing and single-cell RNA-sequencing is a robust approach for mapping developmental lineage trajectories and cell fate changes

Abstract

Mapping the paths that stem and progenitor cells take en route to differentiate and elucidating the underlying molecular controls are key goals in developmental and stem cell biology. However, with population level analyses it is difficult - if not impossible - to define the transition states and lineage trajectory branch points within complex developmental lineages. Single-cell RNA-sequencing analysis can discriminate heterogeneity in a population of cells and even identify rare or transient intermediates. In this review, we propose that using these data, one can infer the lineage trajectories of individual stem cells and identify putative branch points. Clonal lineage tracing of stem cells allows one to define the outcome of differentiation. Integrating these single cell-based approaches provides a robust strategy for establishing and testing models of how an individual stem cell changes through time to differentiate and self-renew.

Keywords: cell fate; lineage; scRNA-seq; single-cell RNA-sequencing; stem cells.

Figure 1.

Integrating single-cell RNA sequencing and lineage tracing can resolve complex cell populations and lineage trajectories. a) Multi potent stem cells can self-renew and give rise to a range of differentiated cell types. Shown are the horizontal basal cell (HBC) stem cells and differentiated cell types in the olfactory epithelium. In this review we focus on the two main lineages of the olfactory epithelium, sustentacular and neuronal, but microvillous cells are a third differentiated cell type. b–f) Circles represent individual cells or clusters of cells as indicated. b) Multipotent stem cells can give rise to multiple cell fate endpoints, and there are branch points along a lineage trajectory where one cell fate path is chosen. Circles colored red represent cell states that are branch points in this schematized lineage. c) Classic genetic lineage tracing, where a stem cell is labeled and its descendants are characterized, can provide insight into stem cell fate potential and the numbers of differentiated cells, different ones of which are indicated by the colored circles. d) scRNA-seq can discriminate the different cell types present in a heterogeneous population: here, we represent a hypothetical example of a reduced dimension plot of scRNA-seq data (inset), and this data can be used by lineage trajectory inference tools to predict the stem cell lineage trajectories, defining the cellular states and predicting lineage trajectory branch points. e) When the cell fate changes are associated with large shifts in gene expression, lineage trajectory inference can fail because the actual cell fate transition violates that assumption that cells that are more similar at the transcriptome level are closer together in the trajectory. Having time-stamping information can allow one to know that a transient state is early in the lineage and proceeds the appearance of later cell states. Incorporating this information into the model can lead to more accurate predictions. The predicted lineage is indicated by the solid lines and arrows. f) If a mature, differentiated cell type (an endpoint) is more similar in gene expression to the starting stem cell fate than an early, transient state, then lineage prediction inference will wrongly order the lineage trajectory progression, indicated here by the dashed lines.

Figure 2.

Workflow for integrating single-cell RNA-sequencing and lineage tracing. We propose the indicated steps to integrate lineage tracing into the design and implementation of the scRNA-sequencing experiments. Clonal lineage tracing can be used to validate and test in silico predictions. By collecting cells at multiple time-points from lineage traced cells, transient states can be identified, and one knows that all cells are derived from the same cell type. scRNA-seq can be used to discriminate the different cell types present within a heterogeneous population. Following clustering of the data to identify cell fates/states, lineage trajectory inference tools can predict lineage trajectories including branch points. Gene expression differences and co-regulated gene expression along the lineage trajectories helps one identify the gene regulatory networks regulating cell fate changes. Predictions regarding lineage trajectory and genetic regulation can be tested with clonal lineage tracing and genetic manipulation.

Figure 3.

Lineage tracing validates lineage trajectory inference for the olfactory HBC stem cell during differentiation. a) To assess the behavior of olfactory HBC stem cells in uninjured tissue, we used an HBC stem cell specific Cre recombinase that coupled genetic ablation of Trp63 (p63), which induces more HBCs to differentiate, with transgenic lineage tracing, and collected cells in a time-course of differentiation. Triangles represent loxP sites that underlie the Cre recombinase-induced conditional knockout of p63 and conditional activation of the eYFP lineage reporter. b) Cells can be visualized in reduced dimension gene expression space. Here, we present a t-distributed stochastic neighbor embedding (t-SNE) plot, and cells are colored by cluster. c) After clustering the cells, we used Slingshot to infer the branching lineage trajectories. Slingshot predicted two bifurcations (arrows), an early bifurcation between the sustentacular and neuronal lineages followed by a second bifurcation of microvillous cells from the neuronal lineage. d) Cells can be ordered along their respective lineages. We present data for the neuronal (left) and sustentacular cell lineage (right). In the top line, cells are colored by their cluster assignment; in the bottom line, cells are colored by the time-point at which they were collected; blue cells are wild-type for p63 and remain in the resting state, and the shade of red represents the time-point (indicated in panel a) of collection after the cells are induced to differentiate. The plots represent the expression of a cell cycle gene set in the neuronal and sustentacular cell lineages. Two clusters in the neuronal lineage (globose basal cells, GBC; immediate neuronal precursors, INP1) show high expression of cell cycle genes, suggesting that the neuronal lineage involves transit through proliferative progenitor fates. e) Clonal lineage tracing of differentiating HBCs demonstrated that most clones were due to an early bifurcation, prior to cell division and included either neurons or sustentacular cells, and neuronal clones were multi-cellular and sustentacular cells could form without cell division. Neurons were distinguished from sustentacular cells by morphology and presence or absence of SOX2 protein expression by immunohistochemistry (magenta). These observations confirmed the main predictions from the branching lineage model derived from Slingshot. Panels a, b, c, and e were adapted with permission.^[57] Copyright 2017, Elsevier.

Figure 4.

Activated state intermediates that are unique to tissue regeneration present challenges to lineage prediction. When gene expression shifts drastically between two cell fates, it contradicts an underlying assumption of all current lineage prediction algorithms that cell fate changes occur as gradual transitions along a continuum. Integrating a lineage tracing time-course into the scRNA-seq analysis can help overcome this obstacle. a) Olfactory HBC stem cell lineage cells. All cells shift away from the resting state (green) at 24-hr post injury (HPI), most to an activated state (blue and gray). The activated state is more distant from the resting state than the sustentacular support cells (magenta). This panel was adapted with permission.^[58] Copyright 2017, Elsevier. b) Predicted branching lineage trajectory of the olfactory HBC stem cell during injury-induced regeneration, with the activated state as the starting point (left). If the activated state is not specified as the starting point in the lineage, then it will be incorrectly designated as an endpoint (right). The lineage tracing derived time-stamping allows us to choose the activated state as the starting point because all stem cells transit to this state upon injury. Clusters/cell types designated as the starting point for the lineage prediction tool, Slingshot, are indicated by an arrowhead; endpoints are indicated by the arrows. c) In traditional stem cell models, stem cells either asymmetrically divide at the individual level or adopt population asymmetry where individual cells either self-renew or differentiate. d) Based on the identification of an activated state that is unique to injury-induced regeneration and that occurs prior to olfactory stem cell self-renewal or differentiation, we propose a modified model of stem cell lineage determination during tissue regeneration.