Studying cellular dynamic processes including the cell cycle, cell differentiation, and cell activation is now possible because to single-cell omics data, which includes transcriptomics, proteomics, and epigenomics data.
Using trajectory inference (TI) approaches, also known as pseudotime analysis, which arrange cells along a trajectory based on similarity in their expression patterns, such dynamic processes can be computationally modelled.
The resulting trajectories are most often linear, bifurcating or tree-shaped, but more recent methods also identify more complex trajectory topologies.
TI methods offer an unbiased and transcriptome-wide understanding of a dynamic process, thereby allowing the objective identification of new (primed) subsets of cells, delineation of a differentiation tree and inference of regulatory interactions responsible for one or more bifurcations.
Current applications of TI focus on specific subsets of cells,but
ongoing efforts to construct transcriptomic catalogs of whole organisms
underline the urgency for accurate, scalable and user-friendly TI
methods.
The first step for TI analysis is subset a group cells for further analysis. In this study, the group cells called Epithelial cells were collected in 6 Pancreatic Ductal Adenocarcinoma (PDAC) samples (Kai Chen et al. (2023))
These cells were then re-clustered and marked with unique marker
genes (see table below). The grouped and annotated cells in the UMAP
plots display the outcome of this stage.
Table 1: List of marker genes for annotation
The UMAP plots show the result of this step with clustered and annotated cells
The goal of slingshot is to use clusters of cells to uncover global structure and convert this structure into smooth lineages represented by one-dimensional variables, called “pseudotime.” It provides tools for learning cluster relationships in an unsupervised or semi-supervised manner and constructing smooth curves representing each lineage, along with visualization methods for each step.
Slingshot consists of two main stages:
The plot show the results of trajectory analysis including four plots:
Boxplot show the pseudotime value following cluters
After running slingshot, we are often interested in finding genes that change their expression over the course of development. We will demonstrate this type of analysis using the tradeSeq package Van den Berge et al. 2020.
For each gene, they will be fit a general additive model (GAM) using
a negative binomial noise distribution to model the (potentially
nonlinear) relationshipships between gene expression and pseudotime. It
will be tested for significant associations between expression and
pseudotime.
Table 2:The table show the expression value oftop 20
genes which were associated with the pseudotime value.
UMAP show the expression of top 4 genes which were asociate with pseudotime
Monocleintroduced
the strategy of using RNA-Seq for single-cell trajectory analysis.
Rather than purifying cells into discrete states experimentally, Monocle
uses an algorithm to learn the sequence of gene expression changes each
cell must go through as part of a dynamic biological process. Once it
has learned the overall “trajectory” of gene expression changes, Monocle
can place each cell at its proper position in the trajectory. You can
then use Monocle’s differential analysis toolkit to find genes regulated
over the course of the trajectory, as described in the section Finding
genes that change as a function of pseudotime . If there are multiple
outcomes for the process, Monocle will reconstruct a “branched”
trajectory. These branches correspond to cellular “decisions”, and
Monocle provides powerful tools for identifying the genes affected by
them and involved in making them. You can see how to analyze branches in
the section Analyzing branches in single-cell trajectories.
The plot show the results of trajectory analysis including four plots:
With the pseudotime value on the x-axis and the cluster label on the
y-axis, the boxplot displays the pseudotime of all cells that follow
clusters. The cells’ development ranged from a low to a high pseudotime
value.
Boxplot show the pseudotime value following cluters
Table 3: Table show which genes have change the
expression following the pseudotime (g-value < 0.05)
UMAP show the expression of top 4 genes which is associated with pseudotime
| Software | Version | Reference |
|---|---|---|
| Slingshot | 2.8.0 | Kelly Street et al. (2018) |
| tradeSeq | 1.14.0 | Koen Van den Berge et al. (2020) |
| monocle3 | 1.3.1 | Junyue Cao et al.(2019) |
| annotation | Marker genes | Protein Atlas, Palloma Porto Almeida et al. (2020), Quan Shen et al.(2018) |