1. Volume 28, Issue 18, Pages 2910-2920.e2 (September 2018)
A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum 
Robert A. Carter, Laure Bihannic, Celeste Rosencrance, Jennifer L. Hadley, Yiai Tong, Timothy N. Phoenix, Sivaraman Natarajan, John Easton, Paul A. Northcott, Charles Gawad 
Current Biology 
Volume 28, Issue 18, Pages e2 (September 2018)
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2. Figure 1 Overview of the Experimental Strategy
(A) Schematic representation of mouse cerebellar development highlighting the time points used in the study as well as the birthdates of the major neuronal cell types (also see Figure S1 for the distribution of UMI counts across replicates and sample dates).
(B) Illustration of the experimental workflow using the 10x Genomics Chromium platform.
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3. Figure 2
scRNA-Seq Facilitates Identification of the Main Cerebellar Lineages
(A) Two-dimensional visualization of the single-cell dataset using t-SNE. Each point represents one cell. Colors represent a specific time point from e10 to P10.
(B) Two-dimensional visualization of single-cell clusters using t-SNE. Cells were clustered using dynamicTreeCut based on the top 1,000 overdispersed genes.
(C) Assignment of clusters to specific cerebellar populations based on expression of known lineage markers. The heatmap shows a selection of genes from the top 100 most discriminatory genes for each cluster.
(D) t-SNE maps (upper panels) highlighting the expression of known lineage markers and their expression in situ (lower panels; data obtained from the Allen Brain Atlas) in the developing cerebellum.
See also Figure S1 for the distribution of UMI counts across samples, Figure S2 for the proportion of cells in each cluster for each sample date across replicates, and Figure S3 for the most discriminatory genes for each cluster.
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4. Figure 3
Relative Proportions of the Main Cerebellar Lineages throughout Development
(A) Relative proportions of each inferred cell type at each of the sampled time points.
(B) t-SNE plot displaying the distribution of the main cerebellar cell types inferred in the full dataset.
(C) t-SNE plots showing the distribution of inferred cell types at each of the sampled time points.
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5. Figure 4 TF Expression during Development of Glutamatergic Lineages
(A) Pseudotemporal ordering of early glutamatergic cells (clusters 5, 23, 39, 21, and 22) by Monocle 2, showing bifurcation of cells into two primary lineages. The dashed line represents the trajectory of GNPs; the solid line represents the trajectory of glutamatergic CN cells. Cells are colored by sample date.
(B) Expression of GNP and glutamatergic CN marker genes along the two primary branches ordered in pseudotime. Dashed lines represent expression in GNPs; solid lines represent expression in glutamatergic CN cells. Cells are colored by sample date.
(C) Branched heatmap showing TFs with highly significant branch-specific expression patterns in pseudotime (q values less than 5 × 10−5). The root of the tree is in the middle of the plot and expression from the earliest to the most differentiated neurons of the glutamatergic CN is progressing to the left while the GNP progression is shown as the heatmap moves to the right of the root.
(D) TF expression correlation network inferred from analyzed glutamatergic populations. Nodes denote TFs expressed in at least 10% of cells. Nodes are linked by an edge if their Pearson correlation coefficient is at least Nodes are colored by the sample date that has the highest mean expression of the corresponding TF.
See also Figure S4 for TF expression during development of GABAergic lineages.
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6. Figure 5
Cell Seek Facilitates Interactive Exploration of the Developing Murine Cerebellum
(A) The main scatterhex plot allows panning and zooming of a two-dimensional view of a dataset, with cells pooled into hexbins. Both gene expression summaries and subgroup information such as sample date and cell type can be visualized.
(B) The correlation network of TFs that show nonrandom expression patterns in the t-SNE plot can be explored using any selected group of cells.
(C) Selected cells can be ordered in pseudotime using Monocle 2 to enable the separation of dyssynchronous expression programs.
(D) TFs that show either branch- or pseudotime-specific expression can be displayed in a heatmap.
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