Trajectory and pseudotime analysis of single cells

We constructed the trajectory of Müller glia in response to retinal injury and performed pseudotime analysis using variable genes through Monocle (51). Pseudotime was calculated by setting unstimulated Müller glia as the root. Unstimulated Müller glia were from P60 mouse, P10 chick, and adult zebrafish. Furthermore, we identified significantly changed genes (or DEGs) along pseudotime. We used the following criteria to identify DEGs: fraction of expressed cells > 0.01, single-cell expression difference > 0.1, and q-value < 0.001. Single-cell expression difference was calculated asSingle-cell expression difference=Q95-expression−Q5-expressionwhere Q95-expression and Q5-expression separtely represent 0.95 and 0.05 quantile of log-transformed single-cell expression values across all bins of pseudotime. We divided cells into 50 bins across pseudotime with equal pseudotime intervals. Bin-derived expressions were obtained by averaging expressions of all cells within each bin.

To identify model-independent DEGs, we also used bin-derived expressions to calculate single-cell expression correlations between two models. Similar to bulk RNA-seq analysis, we identified evolutionarily conserved and species-specific DEGs through cross-species scRNA-seq analysis (table S6).

We aligned Müller glia trajectories from two species using cellAlign (52). In alignment, we separated and sorted different states of Müller glia on the basis of pseudotime. DEGs shared by two species were used for trajectory alignment.