Pseudotime analysis reveals novel regulatory factors for multigenic onset and monogenic transition of odorant receptor expression

During their maturation from horizontal basal stem cells, olfactory sensory neurons (OSNs) are known to select exactly one out of hundreds of olfactory receptors (ORs) and express it on their surface, a process called monogenic selection. Monogenic expression is preceded by a multigenic phase during which several OR genes are expressed in a single OSN. Here, we perform pseudotime analysis of a single cell RNA-Seq dataset of murine olfactory epithelium to precisely align the multigenic and monogenic expression phases with the cell types occurring during OSN differentiation. In combination with motif analysis of OR gene cluster-associated enhancer regions, we identify known and novel transcription (co-)factors (Ebf1, Lhx2, Ldb1, Fos and Ssbp2) and chromatin remodelers (Kdm1a, Eed and Zmynd8) associated with OR expression. The inferred temporal order of their activity suggests novel mechanisms contributing to multigenic OR expression and monogenic selection.


DE Plots
SFigure 2a: Heatmap plot shows the expression profiles of top 10 differentially expressed genes in each cell type starting from HBC0 (top left) and ending with mOSN (bottom right). The gradient scale colors represent the normalized expression in log scale: white (no expression), blue (low to high expression) and red color (very high expression).
SFigure 2b: The upregulated and downregulated (co-)TF genes from stage to stage along neuronal lineage. We selected (co-)TFs with an adjusted p-value less than 0.05 (y-axis, log10 Bonferroni adjusted p-value) and an average expression change of at least 2 fold (x.axis, log2 fold).
10x Genomics dataset Comparison between SMART-Seq2 and 10x Genomics SFigure 4a: Comparison between the expression profiles of the winner and runners up of ORs.
SFigure 4b: Comparison between smart-seq2 and 10x genomics dataset shows the expression profiles of predicted transcription factors (TFs) and chromatin remodelers (CRs) shown on Figure  4b. A,B) shows the expression profiles of TFs. The first violin plot row of each profile of the three groups is from smart-seq2 and the second one is from 10x genomics. C,D) shows the expression profiles of CRs. The first violin plot row of each profile of the three groups is from smart-seq2 and the second one is from 10x genomics.

Coexpression analysis of olfactory receptor genes
SFigure 5: Coexpression analysis of olfactory receptor genes related to the same cluster. We performed MCMC sampling to assess whether the observed average number of OR gene clusters co-expressing at least 2 OR genes is in the expected range. We first construct a binary cells x genes matrix indicating whether or not a gene is expressed in a cell. From this matrix, we compute as a test statistic the relative frequency of clusters expressing at least 2 genes among all clusters. The same is done for a uniform random sample of all binary matrices that have the same marginal count frequencies as the original matrix. The random samples are drawn by the curveball algorithm (see SCode 1 below). The figure shows the histogram of the test statistic for this random sample. The red dotted line indicates the actually observed value, corresponding to a (two-sided) p-value of 0.112. Let expression_mat the original (binarized) genes x cells expression matrix. It contains a 1 whenever the gene is detected in a cell with at least one count. Let assignment_mat the (binary) OR clusters x genes incidence matrix. It contains a 1 at position (c,g) exactly if a gene g is localized in cluster c. Both matrices are available in the Supplementary Materials (Excel 2).

SCode 1: Coexpression analysis of olfactory receptor genes
For each MCMC sample, we record the number of OR genes (co-)expressed in each OR cluster. The test statistic is the relative frequency (in the population of all cells) by which an OR cluster has more than 1 OR gene expressed. plot(location,probabilities,type="h",lwd=4,col="dark grey", xlab="Average #OR clusters per cell containing coexpressed OR genes",

Pseudotime expression profiles for other factors
SFigure 10: A) pseudotime expression profiles of Kdm1a and factors that play a role in changing the function of Kdm1a. B) Feedback Signal. Translation of the newly transcribed OR mRNA activates a co-opted arm of the unfolded protein response (Dalton et al., 2013) and induces a feedback signal (Lewcock and Reed, 2004;Serizawa et al., 2005;Shykind et al., 2004). Atf5 and Adcy3 are proposed to be involved in this process where Atf5 promotes Adcy3 expression which in turn downregulates Kdm1a (LSD1) preventing the de-silencing of another OR gene (Dalton et al., 2013;Lyons et al., 2013).