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. 2023 Nov 10:12:684.
doi: 10.12688/f1000research.134078.2. eCollection 2023.

Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data

Jinming Cheng  1   2 , Gordon K Smyth  1   3 , Yunshun Chen  1   2   4
Affiliations

Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data

Jinming Cheng et al. F1000Res. .

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells.

Methods: This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR.

Results: Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland.

Conclusions: The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Keywords: Single-cell RNA-seq; differential expression analysis; mammary gland; pseudo-bulk; time course analysis; trajectory analysis.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Scatter plots of quality control metrics across all the samples.
Each dot represents a cell. The plots on the left show number of reads vs number of genes detected, whereas those on the right show number of reads vs mitochondria read percentage.
Figure 2.
Figure 2.. UMAP visualization of each individual samples.
The UMAP plots, in sequence from the top row to the bottom row, correspond to E18.5-epi, P5, Pre-puberty, Puberty, and Adult, respectively. In each row, cells are coloured by cluster on the left, by Epcam expression level in the middle, and by doublet prediction on the right.
Figure 3.
Figure 3.. UMAP visualization of the integrated data.
Cells are coloured by cluster on the left and by original sample on the right.
Figure 4.
Figure 4.. Feature plots of the integrated data.
Genes from the top row to the bottom rows are the markers of basal, LP, ML, cycling, and stromal cells, respectively.
Figure 5.
Figure 5.. Bar plot of cell proportion of each cluster in each sample.
Figure 6.
Figure 6.. UMAP visualization of trajectory inferred by monocle3.
Cells are coloured by cluster.
Figure 7.
Figure 7.. UMAP visualization of pseudotime computed by monocle3.
Cells are coloured by pseudotime.
Figure 8.
Figure 8.. Multi-dimensional scaling (MDS) plot of the pseudo-bulk samples labelled by pseudotime.
Samples are coloured by original cell cluster on the left and by developmental stage on the right.
Figure 9.
Figure 9.. A scatter plot of the biological coefficient of variation (BCV) against the average abundance of each gene in log2 count-per-million (CPM).
The square-root estimates of the common, trended and gene-wise NB dispersions are shown.
Figure 10.
Figure 10.. A scatter plot of the quarter-root QL dispersion against the average abundance of each gene in log2 count-per-million (CPM).
Estimates are shown for the raw, trended and squeezed dispersions.
Figure 11.
Figure 11.. Line graphs of expression level of top genes along pseudotime.
The red line represents the predicted expression level in log2-CPM along pseudotime.
Figure 12.
Figure 12.. Heatmap of top 20 up and top 20 down genes.
Rows are genes and columns are pseudo-bulk samples.
Figure 13.
Figure 13.. Bar plot of −log 10 p-values of the top 10 down-regulated GO terms under each GO category.
Figure 14.
Figure 14.. Bar plot of −log 10 p-values of the top 15 down-regulated KEGG pathways.
Figure 15.
Figure 15.. A smooth curve of PI3K-Akt signaling pathway expression level against pseudotime.
See this image and copyright information in PMC

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