Molecular characterization of projection neuron subtypes in the mouse olfactory bulb
- Department of Neuroscience, Division of Biology and Medicine, and the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, United States
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, and CNRS UMR 7241 and INSERM U1050, France
- The Francis Crick Institute, Sensory Circuits and Neurotechnology Laboratory, United Kingdom
- Department of Neuroscience, Physiology & Pharmacology, University College London, United Kingdom
- Janelia Research Campus, Howard Hughes Medical Institute, United States
- The Francis Crick Institute, Bioinformatics and Biostatistics, United Kingdom
- The Francis Crick Institute, Scientific Computing - Digital Development Team, United Kingdom
- Inria Antenne Lyon La Doua, France
- Université de Lyon, INSA-Lyon, LIRIS, UMR 5205, France
Figures
Figure 1 with 2 supplements
Comprehensive molecular profiling of olfactory bulb projection neurons.
(A) Schematic representation of experimental design. Top: after injection of rAAVretro-CAG-H2B-GFP into PCx and AON, single nuclei were dissociated from three mice (single nuclei (sn) R1,2,3: …
Figure 1—figure supplement 1
Schematic representation of olfactory bulb cell types and their cortical projection targets.
(A) Schematic representation of cell types and their distribution within the olfactory bulb (IN: interneuron, TC: tufted cell, MC: mitral cell, GL: glomerular layer, EPL: external plexiform layer, …
Figure 1—figure supplement 2
Enrichment of GFP-expressing nuclei using fluorescence-activated nuclei sorting (FANS).
(A) Representative FANS data of GFP-expressing nuclei after injection of rAAVretro-CAG-H2B-GFP into AON and PCx to label OB projection neurons. From left to right: gating strategy for enrichment of …
Figure 2 with 1 supplement
Single-nucleus RNA sequencing distinguishes distinct cell types and molecular signatures of OB projection neurons.
(A) UMAP representation of gene expression profiles of 31,703 single nuclei combined from all replicates (R1, R2, R3) of mice injected into both AON and PCx, grouped into 22 clusters color-coded by …
Figure 2—figure supplement 1
Quality check of individual replicates of sn-RNA seq (sn-R1/R2/R3 dataset) shows the reliability of the data and the replicability of each cell type.
(A) Depiction of nuclei for each replicate (in red R1, in green R2, in blue R3) embedded in the UMAP space showing that replicates are very similar to each other and can be combined for downstream …
Figure 3 with 3 supplements
Histological validation of molecularly distinct mitral and tufted cell types.
(A) Combined average (avg) raw expression level of the top n differentially expressed (DE) genes for each mitral cell type (M1 n=14, M2 n=11, M3 n=10), overlaid onto the subclustering UMAP space …
Figure 3—figure supplement 1
Histological analysis of DE genes for distinct mitral cell types.
(A) Schematic representation of the smFISH images for validating selected mitral cell type-specific marker genes upon rAAVretro-CAG-H2B-GFP injection into PCx and AON. The scheme depicts the laminar …
Figure 3—figure supplement 2
Histological analysis of DE genes for distinct tufted cell types.
(A) Schematic of the smFISH images for validating selected tufted cell type-specific marker genes upon rAAVretro-CAG-H2B-GFP injection into PCx and AON. The scheme depicts the laminar location …
Figure 3—figure supplement 3
Excitability-related, cell adhesion-related, and pan-OB projection neuron DE genes.
(A) Violin plots showing maximum raw expression values of genes encoding for channels as key candidates for controlling the differential excitability of different MC and TC types. Kcng1 gene is …
Figure 4 with 3 supplements
Mitral and tufted cell-specific regulons combine into modules.
(A) Schematic representation of the network analysis pipeline, including the required input, the SCENIC protocol, and the output in the form of regulon modules and a regulatory network (in Figure 5).…
Figure 4—figure supplement 1
Comparing transcriptomic and gene regulatory network-defined mitral and tufted cell types.
(A) Transcriptome-defined mitral and tufted cell types in transcriptome-based UMAP space (Figure 2D) shown here for comparison with (B). PG cluster was not used in the pySCENIC gene regulatory …
Figure 4—figure supplement 2
Mitral and tufted cell type-specific marker genes found in regulons.
(A) Mitral and tufted cell type-specific marker genes identified as regulons in pySCENIC analysis. Columns from left to right: cluster membership; marker gene; marker gene as regulon with its max …
Figure 4—figure supplement 3
PAGA-based trajectory analysis of mitral and tufted cell types.
(A) Recomputed transcriptome-based UMAP with the PG cluster removed from the data. (B) The PAGA graph derived from the clustering in (A). (C) Average regulon activity along the trajectory ET4, ET1, …
Figure 5 with 2 supplements
Mitral and tufted cell-specific transcription factor network derived from regulons.
(A) Overview of mitral and tufted cell-specific transcription factor (TF) network, with node size scaled by the number of target genes and nodes colored with different shades of gray based on the …
Figure 5—figure supplement 1
Top five mitral and tufted cell type-specific regulons.
(A) Regulon specificity score for each cell type, with the top five highest scoring regulons shown in red. Specificity scores are based on Jensen-Shannon Divergence, a metric for comparing …
Figure 5—figure supplement 2
Mitral and tufted cell type-specific marker genes visualized as target genes in the gene regulatory network.
For each mitral and tufted cell type, marker genes are integrated in the gene regulatory network (network shown in Figure 5), with nodes and interactions colored by the highest confidence cut-off …
Figure 6
Regulon-based simulations from bulk RNA deep sequencing data suggest that mitral cell types have distinct projection targets.
(A) Schematic representation of strategy to integrate bulk RNA-seq and single nucleus RNA-seq data. (A1) Simulations use regulons inferred from sn-R1/R2/R3 data. A regulon consists of a …
Figure 7
Targeted snRNA-seq experiment validates simulation-based predictions of molecularly defined-mitral cell types with distinct projection targets.
(A) Schematic representation of experimental design. Top: after injection of rAAVretro-CAG-H2B-GFP into PCx, single nuclei were dissociated from one mouse and sorted using Fluorescence-activated …
Author response image 1
Schematic representation of experimental design and Coronal section.
(A) Schematic representation of experimental design. Top: after injection of rAAVretro-CAGH2B-GFP into PCx, single nuclei were dissociated from 1 mouse and sorted using Fluorescence-activated Nuclei …
Author response image 2
Each row represents one cell in the new, targeted snRNA-seq data.
Each column represents one of 1000 linear discriminant analysis (LDA) classifiers trained to discriminate projection neurons from other cell types (analogously to the analysis in the original …
Author response image 3
Linear discriminant analysis (LDA) classifiers were trained on the original, larger snRNA-seq dataset, then used to predict the cell type identity of projection neurons in a new, targeted snRNA-seq dataset.
Each row represents one projection neuron in the new, targeted sn-PCx data. Each column represents one of 1000 linear discriminant analysis (LDA) classifiers trained to predict cell type identity …
Author response image 4
Seurat marker genes identified as regulons in pySCENIC.
Columns are cell type, marker gene, and the marker gene as regulon with its max confidence cutoff. Any regulon with a confidence cutoff <0.3 is ignored.
Author response image 5
Presence of marker genes identified through Seurat as target genes in regulons of SCENIC.
Columns from left to right are: cell type; marker gene; presence in regulon(s) with corresponding confidence cutoffs. Any regulon with a confidence cutoff <0.4 is ignored.
Author response image 6
Integration of nuclei from R1/R2/R3 snRNA-seq with cells from Tepe et al.
2018 dataset.(A,B) Integration of WT replicates from the Tepe et al. study with the integrated R1/R2/R3 snRNAseq dataset of the present study. Count data for samples WT1 and WT2 were extracted from …
Author response image 7
On the left is shown the number of genes per cell in the Tepe et al.
, 2018 dataset while on the right the equivalent QC parameter (gene/nucleus) in our R1/R2/R3 snRNAseq dataset.
Author response image 8
UMAPs for different numbers of neighbors (nb, rows) and different Louvain resolutions (res, columns).
Neighbors are taken at 25, 50 and 100; resolutions at 0.2, 0.3, 0.5, and 0.7. We recomputed the PCA space of the projection neuron subcluster and consequently the displayed clustering is slightly …
Author response image 9
UMAPs of the reviewer’s requested TFs: Sox10, Sp1, Fos, Jun, Egr1.
All TFs, with the exception of Sox10, are expressed in the projection neurons. We note that Sox10 is sparsely expressed and was of minor importance in both the original manuscript and the current, …
Author response image 10
Presence of genes known for their role during OB development in regulons of SCENIC.
In the left panel, columns are: developmental gene; presence as target gene in given regulon with max confidence cutoffs. Any regulon with a confidence cutoff <0.5 is ignored. In the right panel: …
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (M. musculus) | C57Bl/6 | Jackson Laboratory | Stock #: 000664 RRID:IMSR_JAX:000664 | |
| Genetic reagent (Adeno-associated virus) | rAAVretro-CAG-H2B-GFP | Tervo et al., 2016 | PMID:27720486 | |
| Commercial assay or kit | Nuclei PURE Prep | Sigma | cat#: NUC201-1KT | |
| Commercial assay or kit | Arcturus PicoPure RNA Isolation Kit | ThermoFisher | cat#: KIT0204 | |
| Other | DRAQ5 | ThermoFisher | cat#: 65-0880-92 | |
| Software, algorithm | Cell Ranger version 3.0 | 10x Genomics | RRID:SCR_017344 | |
| Software, algorithm | Seurat v3.6 | Butler et al., 2018 | RRID:SCR_007322 | |
| Software, algorithm | Scanpy v1.7.1 | Wolf et al., 2018 | RRID:SCR_018139 | |
| Software, algorithm | glmGamPoi R-package | Ahlmann-Eltze and Huber, 2021 | PMID:33295604 | |
| Software, algorithm | SCENIC | Aibar et al., 2017 | RRID:SCR_017247 | |
| Software, algorithm | Cytoscape | Shannon et al., 2003 | RRID:SCR_003032 | |
| Software, algorithm | ImageJ version 2.1.0. | Schindelin et al., 2012 | RRID:SCR_003070 |
