Fig. 2: Pseudo-time-space (PSTS) trajectory analysis and validation in a mouse model of traumatic brain injury (TBI). | Nature Communications

Fig. 2: Pseudo-time-space (PSTS) trajectory analysis and validation in a mouse model of traumatic brain injury (TBI).

From: Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Fig. 2

a Schematic showing the cortical impact site and microglia activation. b Spatio-temporal trajectory of microglial activation at 3 days post-TBI, as predicted by our PSTS algorithm, running from the hypothalamus (node 4), through the thalamus (node 2) and hippocampus (node 3) and then the cortical penumbra regions adjacent to the lesion core (nodes 1). Colour-coded pseudo-time-space values (ranging from 0 to 1) reflect microglia-related gene expression changes through the tissue space. c Clustering results for TBI Visium ST data (n = 2442 spots). d Transition genes positively (blue) or negatively (red) correlated with the predicted trajectory for microglia activation (extracted by Spearman correlation test of pseudo-time-space values; adjusted p-value < 0.05 and correlation coefficient >0.3 or <−0.3). e Enrichment analysis of upregulated transition genes revealing significant pathways related to microglia activation, inflammation and neural injury. f Experimental validation of the spatio-temporal trajectory for microglia (green) activation following TBI; cell nuclei are shown in blue. Imaging was performed across five different brain regions of interest (ROIs; from one brain per time point), equivalent to the trajectory nodes, from sham (uninjured) controls and five different time points post-TBI. Note the changes in microglia abundance and morphology across cluster nodes and time. g Density plots illustrating changes in microglia cell body size (proxy for activation) over time (top) and space (bottom; 3 days post-TBI only). h Changes in microglia density over time and space for all ROIs (n = 4 biological replicates per time point; error bars show SEM. i Variograms depicting the autocorrelation of PSTS/pseudotime values for each spot. Plots show the spatial variance in PSTS/pseudotime values produced by Slingshot, Monocle 3 and PSTS. Lower values of the semi-variance Matheron estimator indicate higher PSTS/pseudotime continuity in the spatial context, and thus a more likely trajectory (see “Methods”); PSTS semi-variance is indicated by the red dashed line. j Spatial branching patterns for microglia activation using different trajectory analysis methods. Only PSTS predicted a trajectory leading to the penumbra regions rather than the core (where microglia are mostly absent; see inset and also Figs. S5 and S6).

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