# Developmental scRNASeq Trajectories in Gene- and Cell-State Space--The Flatworm Example
## 3 Results  
### Portrayal of Developing Single Cell Transcriptomes  
For the comprehensive cartography of cell states upon tissue development of planarian, we generated SOM transcriptomic portraits of all nearly 22,000 cells studied. Then, we summarized them into mean portraits of each of the 51 cell types defined previously [29] by averaging all single cell-portraits of each type. tSNE plots were used for an overview of transcriptional diversity of the SOM portraits (Figure 2A). Cells were grouped into 51 clusters in accordance with the classification and coloring scheme used previously [29] (see legend in Figure 2E). Our SOM-based results are in line with that of [PERSON] et al. [29] showing one large cluster of neoblast stem-cells (grey color) which is surrounded by clusters of differentiated cells of different tissues (dark color tones) and clusters of progenitor cells (brighter colors) in between (Figure 2A). The \"differentiation\" axis points roughly from the stem cells in the right part toward differentiated cells in the left. The SOM expression portraits of the cell types are characterized by red and blue spots at specific positions. They refer to clusters of concertedly over- and under-expressed genes, respectively. For example, neoblasts show a red overexpression spot in the upper right corner of the map, while differentiated muscle and goblet cells show overexpression spots in the left and middle bottom area, respectively (Figure 2B). The respective progenitor cell express a virtually bimodal spot patterns consisting of a mixture of the spots observed in the undifferentiated and differentiated cells, respectively. One can understand differentiation as a series of activated cellular programs, which become evident as path in the expression landscape linking the stem cell spot with the spot observed in differentiated cells. Examples of such developmental trajectories of changing gene states were shown in Figure 2D for muscle, gut, epidermal, and neuronal cells obtained by means of the maximum flow algorithm (see Materials and Methods section). Changing cell states upon differentiation were visualized using URD-clustering. This method sorts cells according to mutual similarities along pseudotime trajectories, which are specific for different developmental paths (Figure 2C).  
In summary, we here introduced two complementary views on developmental trajectories, namely as paths describing the sequence of cellular states passed between stem cells and differentiated tissue cells on one hand and as the sequence of major gene regulatory programs activated upon differentiation on the other hand. SOM-space visualizes the latter aspect. It enables studying functional aspects of development in gene space.  
Figure 2: SOM portrayal of the developing planarian single cell transcriptome: (**A**) Single-cell resolved tSNE plot (cell types are color-coded as shown in the legend in part E). SOM expression portraits of selected cell types provide a glimpse on cell and gene transcriptional space. (**B**) Expression portraits of stem cells and of differentiating progenitor and differentiated muscle body and a goblet cells show overexpression spots at different positions as indicated by the dashed circles. The spot profile reveals specific up regulation of the spots in the respective tissue specific cells. (**C**) The URD [40] plot resolves multibranched single-cell developmental manifolds. Paths of selected tissue types are indicated by arrows. (**D**) Gene activation trajectories of cell differentiation from stem cells into four different tissues were calculated by means of maximum flow on SOM embedding. (**E**) Legend of colors used for cell-type assignment (see also [29]). The color code is used throughout the paper.