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Review
. 2021 Oct 4;1(6):100087.
doi: 10.1016/j.crmeth.2021.100087. eCollection 2021 Oct 25.

Computational tools for analyzing single-cell data in pluripotent cell differentiation studies

Jun Ding  1 , Amir Alavi  2 , Mo R Ebrahimkhani  3 , Ziv Bar-Joseph  2   4
Affiliations
Review

Computational tools for analyzing single-cell data in pluripotent cell differentiation studies

Jun Ding et al. Cell Rep Methods. .

Abstract

Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.

Keywords: computational approaches; pluripotent cell differentiation; protocol optimization; single cell; stem cell applications.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
An end-to-end approach for using single cell technologies in iPSC studies Top: computational models for optimizing cell differentiation protocols. These methods fall into four different categories. (1) Time series iPSC single-cell experimental design. (2) Clustering/cell-type annotation: using marker genes and annotation datasets to determine cell types at different stages in the analysis. (3) Trajectory inferences: reconstructing cell differentiation trajectories (from iPSCs) to identify the different fates and their onset. (4) Regulatory and signaling network inference: using epigenomics and spatial single cell data to infer the underlying regulatory network within cells and the cell-cell interaction networks. Middle: protocol validation and reproducibility. Cell alignment methods can compare datasets to human reference or between repeated studies. Reproductivity metrics are used to estimate the similarity between multiple datasets (e.g., from different runs). Barcoding methods are used to determine the cellular trajectories and timing of the cell fate decisions. Bottom: applications. Generated cells can be studied to determine longevity and potential cancer risk. Cells can then be either used to replace damaged cells or for studying the impact of treatments on individuals.
Figure 2
Figure 2
Alignment of scRNA-seq data from a large iPSC single-cell study. Results are presented for the top three human iPSC donors in terms of cell counts (“joxm,” 1,415; “guss,” 1,093; “poih,” 1,077). (A and B) UMAP embeddings of the unaligned cells, colored by donor ID (A) and differentiation time point (B). (C and D) UMAP embeddings of the cells after integration with Seurat, colored by donor ID (C) and differentiation time point (D). (E) Evaluation of the alignment, quantified using the local inverse Simpson’s index score based on PCA coordinates after integration with Seurat (the same PCA embeddings used to produce the UMAP embeddings) in (C and D). The top part shows a high mixing score for donors, indicating that the alignment successfully overcomes batch affects. The bottom score shows low mixing for time points, indicating that the alignment correctly separates cells based on their differentiation stage.
Figure 3
Figure 3
A typical dimensionality reduction-based trajectory inference pipeline The high-dimensional cell expression profiles (blue dots, top) are first projected to a lower space (bottom, 2D in this example). Next, a trajectory is inferred by connecting anchor nodes (gray dots), which represent cell clusters. In most methods the initial (or starting) set of cells are identified by the user and serve as the starting point for the trajectory (left most gray node). Next, this point is linked to other points by edges to construct the full trajectory (edges from left to right).
Figure 4
Figure 4
CSHMM results for an iPSC data on lung alveolar epithelial cell differentiation, in GEO: GSE145539 (A) UMAP plot of all clusters. (B and C) HOPX expression (alveolar type 1 marker) in different clusters. HOPX expression is higher in clusters 2, 6, and 8 CSHMM trajectories. (D) CSHMM reconstructed trajectories. Each dot represents a cell, while colors for cells correspond to the clusters in (A) HOPX expression is highest for later branches. TFs are assigned by the method to some edges of the differentiation tree. For example, EGR1 regulate the cellular state transitions out of the cluster 0.
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