Combining single-cell RNA-sequencing with a molecular atlas unveils new markers for Caenorhabditis elegans neuron classes

Abstract

Single-cell RNA-sequencing (scRNA-seq) of the Caenorhabditis elegans nervous system offers the unique opportunity to obtain a partial expression profile for each neuron within a known connectome. Building on recent scRNA-seq data and on a molecular atlas describing the expression pattern of ∼800 genes at the single cell resolution, we designed an iterative clustering analysis aiming to match each cell-cluster to the ∼100 anatomically defined neuron classes of C. elegans. This heuristic approach successfully assigned 97 of the 118 neuron classes to a cluster. Sixty two clusters were assigned to a single neuron class and 15 clusters grouped neuron classes sharing close molecular signatures. Pseudotime analysis revealed a maturation process occurring in some neurons (e.g. PDA) during the L2 stage. Based on the molecular profiles of all identified neurons, we predicted cell fate regulators and experimentally validated unc-86 for the normal differentiation of RMG neurons. Furthermore, we observed that different classes of genes functionally diversify sensory neurons, interneurons and motorneurons. Finally, we designed 15 new neuron class-specific promoters validated in vivo. Amongst them, 10 represent the only specific promoter reported to this day, expanding the list of neurons amenable to genetic manipulations.

Figure 1.

t-SNE projection and neuron-class assignment principle. (A) Single cell profiles are projected in two dimensions using t-SNE. Cell-clusters corresponding to non-neuronal cells or to neurons are distinguished by color and label. (B) Neuron classes assignment to clusters using the z-score method. Gene's z-scores from the centred scaled dataset are used to assign neuronal classes to the clusters. A combined z-score (Stouffer's method) for all neuronal classes is obtained for each cell in the cluster, in this case PLN, ALN, SDQ and cluster 20. (C) Neuron classes are treated as independent groups and ranked by the median value of their combined z-scores. In the notched box plots, the notches display the 95th confidence interval around the median (black horizontal line); the box contains the interquartile and the whiskers extend to the most extreme data points which are no more than 1.5 times the interquartile range from the box. A sequential t-test from the highest to lowest ranked neuron class indicates where the assignment of neuron classes should stop (P<0.05). An additional false discovery rate (FDR) filter of <5% is applied. (D) Clusters assigned to a single neuron class are labelled in green (e.g., DVA); clusters assigned to a subset of molecularly similar neuron classes are labelled in blue (e.g. 36, corresponding to URX, AQR and PQR). (E) Based on their combined z-score, clusters 41 is assigned to the DVA neuron class; clusters 61 and 38 are assigned to ASI and ASJ, respectively; clusters 28, 36 and 4 are assigned to a set of molecularly similar classes of sensory neurons, which we could or could not further segregate with clustering iterations.

Figure 2.

Multiple iteration clustering approach and results. (A) Each parent cluster from the first iteration clustering was treated independently for re-clustering for all combinations of parameters ranging from 3 to 92 PCs and resolution from 0.1 to 3. The neuron classes assigned to every sub-clusters generated by this large number of re-clustering trials are analysed. A consensus is reached for parent cluster 26: it suggests the re-clustering of cluster 26 should generate 3 sub-clusters. These three sub-clusters should be assigned to AFD, AWC and AWB/AWC. This result is observed for PCs: 23/resolution: 1, used for representation and for gene expression lists. (B) Uniform Manifold Approximation and Projection (UMAP) projections are used to represent the second or third iteration clustering from parent clusters containing multiple neuron classes. Several of sub-clusters can be assigned to a single neuron classes using the combined z-scores.

Figure 3.

Clusters assignment by combined z-score and multiple iterations. For each cluster, the 10 best-ranked combined z-scores are displayed as well as the quality of the assignment consensus (%) reached by the re-clustering iterations of the parent cluster. These two assessments helped us to assign each cluster to one or several neuron class. The automated assignment by ranked z-scores is displayed by the red colour within the notched box, gray if discarded for final assignment, green for manual assignment. The first iteration clusters automatically assigned to a single neuron class identity are shaded in light green. Clusters assigned through iterations are shaded in pink. Clusters detailed in pseudotime are shaded in orange. Clusters poorly assigned to any neuronal class are shaded in gray.

Figure 4.

AVK profiling overlap and reporter strain design strategy. (A) We observed an overlap of 35 genes between the 100 most enriched genes in cluster 14 assigned to AVK neuron class and the 100 most enriched genes from the AVK neuron bulk sequencing (adj. pv = 9.82e–40; P-value adjusted by Bonferroni correction). (B) The fold change comparison highlights the genes co-enriched in cluster 14 and in AVK bulk sequencing profile. The expression of nlp-49 and flp-1 mRNA in AVK were previously described (purple) (12,48). Several new potential AVK markers are highlighted in red, including twk-47. (C) Generation of a reporter strain expressing a 250 bp twk-47 promoter fused to the mKate sequence. The confocal image shows the specific expression of mKate in the AVK neurons.

Figure 5.

Predictions of cell fate regulators. (A) We identified transcription factors enriched in neuron class and differentially expressed between sister cells. Based on these rules, predicted cell fate regulators for neuron classes’ differentiation are displayed. The expression pattern of 16 of these predicted cell fate regulators are indicated by color and gene name for three lineages. The 16 transcription factors in bold and underlined were also previously proven experimentally to contribute to neuron class differentiation. (B) Expression of an RMG molecular identity marker (Ex[Pnlp-56::RFP] transgenes) was analyzed in unc-86(n846) animals and in unc-86(n846) animals rescued by an UNC-86 fosmid. UNC-86 is required for expression of the RMG marker. 81% of the non-rescued unc-86(n846) had no or faint expression of Pnlp-56::RFP in RMG. Differential-interference contrast suggests neurons are present at the position of FLP and RMG in unc-86(n846). Scale bar: 30 μm.

Figure 6.

Pseudotiming reveals the maturation of PDA neurons. (A) Pseudotiming connects cluster 42.1 (late Y cell) to cluster 56 (PDA neuron) by an inferred trajectory (angled arrow). The expression level of some markers along this abstract trajectory is reported in RNA count. We report expression of markers for the Y cell (egl-20, sem-4, unc-33 and egl-27) on one side and markers for the PDA neuron (ceh-6, cog-1 and cha-1) on the other side. Markers for synaptic genes (sad-1/SAD kinase, unc-64/Syntaxin) and guidance genes (unc-5/Netrin Receptor, madd-4/Punctin) are also reported. (B) For pseudotiming of cluster 3, we report expression levels for markers of DD (flp-13) and VD (oig-1) motorneurons as well as the gabaergic marker unc-47 and a potential maturation marker: kcc-2. (C) For pseudotiming of cluster 4, we report expression levels for markers of ALM_PLM (mec-12 high) and PVM (flp-8) neurons as well as a potential marker of maturation: alr-1. (D) For pseudotiming of cluster 16, we report expression levels for markers of FLP (asic-1, lin-14), FLP_PVD (dma-1, mec-10) or PVD (ser-2) neurons. (E) Pseudotiming connects clusters 0 to cluster 2 by an inferred trajectory along which we report expression levels for markers for the DA, VA, VB, DB, AS, SAB motorneurons. For all pseudotiming, the location of the putative immature precursors and their maturation is displayed on the trajectories by the green circle and arrow, respectively.

Figure 7.

Confocal images of 17 designed reporter strains. The expression patterns of mKate (red) or mEGFP (green) driven by the corresponding gene promoter were analysed by confocal microscopy. White arrowheads indicate the neuron-classes expressing the fluorescent proteins. The animals were imaged between L2, L4 or young adult (YA) developmental stages. Details concerning neuron class identification, promoters’ size and primers’ sequences are described in Supplementary Figure S2.