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. 2022 Mar 7;23(1):73.
doi: 10.1186/s13059-022-02629-7.

TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

Dongshunyi Li #  1 , Jeremy J Velazquez #  2   3 , Jun Ding  4 , Joshua Hislop  2   3   5 , Mo R Ebrahimkhani  6   7   8   9 , Ziv Bar-Joseph  1   10
Affiliations

TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

Dongshunyi Li et al. Genome Biol. .

Abstract

A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids.Software https://github.com/doraadong/TraSig .

Keywords: Cell-cell interactions; Development; Gene expression.

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Conflict of interest statement

M.R.E and J.J.V. have a patent (WO2019237124) for the organoid technology used in this publication.

Figures

Fig. 1
Fig. 1
Example cases where the average expressions of the ligand and receptor that are known to interact are the same. Of these four figures, only the last two represent correlated activation and repression of these proteins. Methods that only use the average expression of genes in clusters cannot differentiate between these 4 profiles and so will score all of them the same
Fig. 2
Fig. 2
TraSig workflow. Top left: For a scRNA-Seq dataset, we use the reconstructed pseudotime trajectory and the expression data as inputs. Bottom left: We next determine expression profiles for genes along each of the edges (clusters) using sliding windows and compute dot product scores for pairs of genes in edges. Right: Finally, we use permutation tests to assign significance levels to the scores we computed
Fig. 3
Fig. 3
CSHMM and TraSig’s results on the liver organoid data. a Reconstructed trajectory for scRNA-Seq profiled at day 5, day 11, and day 17 from pluripotent stem cell (PSC)-derived multilineage human liver organoids generated as previously described [11]. CSHMM reconstructs a tree-structured trajectory that clusters cells to edges based on their expression patterns and relationship to the expression patterns of prior edges (Methods). Cells are colored by their cell type labels and are shown as dots ordered by their pseudotime assignment. DesLO, designer liver organoid; HL, hepatocyte-like cells; DL, ductal/cholangiocyte-like cells; SL, stellate-like cells; EC, endothelial-like cells; PL, progenitor-like cells; WT, wild type. b, c UMAP [13] visualizations for day 11 and day 17 cells, colored by cell type labels. d Heatmap for scores assigned by TraSig to cluster pairs containing cells sampled at the same time. e Sliding window expression for four example ligand-receptor pairs predicted to interact by TraSig
Fig. 4
Fig. 4
Ligand-receptor interaction predictions from TraSig of interest for functional studies. a Cartoon of cell signaling interaction between different DesLO cell types (HLC, hepatocyte-like cells; CLC, cholangiocyte-like cells; SLC, stellate-like cells; ELC, endothelial-like cells). b Trajectory plot showing cell type assignments with key identifying genes highlighted by different colors (red = SOX2+ non-induced cells, yellow = SOX9 cholangiocyte-like cells, blue = hepatocyte-like cells, purple = stellate-like cells, green = endothelial-like cells). c Sender CXCL12 cells from the cholangiocyte and stellate populations in red shown with the receiver CXCR4 expressing endothelial cell population in blue. d Sender and receiver signaling populations (red = senders/ligands; blue = receivers/receptors). The darker the color is, the higher the expression level in a cell
Fig. 5
Fig. 5
Functional validation of TraSig ligand-receptor signaling predictions. a Strategy for localized signaling effect of CXCL12. CXCL12 (red stain) overlaid with CD34 (green stain, on insets only) shown here with the yellow boxes indicating loci of high relative CXCL12 expression, and blue boxes indicating low relative CXCL12 expression. The same strategy was used for VEGF loci selection (see the “Methods” section). b Percent vessel area and junction density measured at CXCL12 and c VEGF low vs high loci from day 14 liver organoid cultures using AngioTool. n = 4 loci for high CXCL12/VEGF expression and n = 4 loci for low CXCL12/VEGF on one coverslip per staining combination. d Example of AngioTool evaluation of CD34-stained liver organoid cultures from the vehicle control (top) and Axitinib (bottom) conditions. e Percent vessel area, junction density, and average vessel length vascular metrics determined by AngioTool analysis results of CD34-stained liver organoid cultures with different inhibitor conditions. n = 2 biological replicates with 4 sampled areas per coverslip. For b and c, unpaired two-tailed t test was used, * p<0.05, **** p<0.0001. For e, ANOVA with Tukey post-comparison test was used, **** p<0.0001. Data are represented as mean ± SE for b, c, and e
Fig. 6
Fig. 6
Results from comparing TraSig with SingleCellSignalR and CellPhoneDB. a Heatmaps for scores assigned by the three different methods for all cluster pairs representing cells sampled at the same time. b − log10p-value for enriched GO terms related to endothelial cells and vascular development. c Venn diagrams for the overlap in identified ligand-receptor pairs among the three methods. The overlap between TraSig and SingleCellSignalR is high though roughly 50% of the identified pairs by each method are not identified by the other
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