Transcriptional correlates of SVZ NSCs regionalization
As described above, a multitude of external factors (diffusible and cell-contact factors) act in concert to orchestrate NSCs activity and regionalization throughout pre- and postnatal development. Cross-talks exist at multiple levels in between these pathways resulting in the regional expression of define gene networks that regulate NSCs activity, as well as their production of distinct cell progenies (see for exemple Fiorelli et al., 2015; Azim et al., 2016). Understanding the transcriptional specificities of postnatal NSCs have proved to provide a wealth of information on their regional diversity.
Transcriptional profiling of NSCs have classically been performed on a population of cells (bulk analysis), even if recently developed RNA-sequencing approaches are now becoming more popular and provide additional information (see general discussion).
For bulk analysis, cell populations of interest are enriched by FACS, based on their expression of specific markers. Their mRNA is extracted then analyzed by microarrays, qPCR or RNA sequencing approaches. This approach gave deeper insights into transcriptional differences that exist in NSCs isolated from different regions, and/or at distinct stages of activation. Comparisons of transcriptional profiles of DG (Dentate Gyrus) and SVZ NSCs identified IGF2 to be highly enriched in the DG. In this region, IGF2 was shown to control the NSCs proliferation through AKT-signaling (Bracko et al., 2012). In the SVZ the early expression of neuronal markers in NSCs has been demonstrated, in agreement with an early priming of NSCs to differentiate into specific lineage. Further, the importance of cilia- and Ca-dependent pathways were emphasized (Beckervordersandforth et al., 2010). In another study, EGF receptor expression was used to isolate activated and quiescent NSCs from the SVZ. This allowed discovering multiple markers for these two stages and uncovered signaling pathways that may be targeted by small bioactive molecules to regulate NSCs activity (Codega et al., 2014).
We recently performed a similar transcriptional analysis to gain insight into the transcriptional correlates of SVZ NSCs. In order to probe heterogeneity of the postnatal SVZ, we microdissected its dorsal and lateral walls at different postnatal ages and isolated NSCs and their immediate progeny based on their expression of Hes5EGFP/Prominin1 and Ascl1-EGFP, respectively. Whole genome comparative transcriptome analysis revealed transcriptional regulators as major hallmarks that sustain postnatal SVZ regionalization. This comparative analysis, which was also applied to the NSCs and TAPs they contain, revealed the existence of an unsuspected heterogeneity in the postnatal SVZ, at both the regional and temporal levels (Azim et al., 2015). This was evidenced by the surprisingly high number of genes differentially expressed in the microdissected dorsal and lateral SVZ. Roughly, 30% of the genes were age-specific, whereas 30%–40% were shared by all three ages analyzed. Classification based on GO terms revealed the importance of transcription-related cues that were further abundant in NSCs. We identified several other transcriptional regulators aside from TFs. Those include chromatin modifiers, epigenetic modulators, downstream signaling mechanisms (nuclear), all of which could act in concert to dynamically regulate the diversity of neuronal and glial lineages generated by SVZ-NSCs during early postnatal life.
Meta-Analysis of Transcriptional Profiles
To generate the lists of TFs that are enriched in dNSCs and lNSCs, we made use of previously published datasets (Cahoy et al., 2008; Azim et al., 2015), accessible on the Gene Expression Omnibus database (GEO: GSE60905 and GSE9566). We analyzed them on the “Gene Expression Omnibus” (https://www.ncbi.nlm.nih.gov/geo/) for transcripts that are differentially expressed between dNSCs and lNSCs (≥1.8 fold enrichment and p-values <5%). Finally, we selected transcripts for transcription factor activity and regulation of transcription using “DAVID Analysis Wizard” (https://david.ncifcrf.gov/). Lists of transcripts were analyzed for enrichments in the neuronal, astrocytic or oligodendrocytic lineage using the transcriptional dataset of the Barres group (Cahoy et al. 2008; GSE9566). Heatmaps were produced using a self-made R script “Heatmap Generator” which is described below.
We developed a tool to combine different transcriptomic datasets available via the “Gene Expression Omnibus” (GEO) and generate heatmaps named HeatMap Generator. This tool is a self-made R script that can be run as a local application. The procedure involves to compile on the one hand the datasets of interest with their GSE references, and on the other hand the genes of interest. Both files should be saved as csv files with the same name and placed in a DataSet and Genes folders accordingly. Then the application GSEtoHeatmaps.sh can be launch. The software will interrogate the GEO database and generate heatmaps of pre-selected genes. The R script is provided as Annex 1.
Meta-Analysis of Single-Cell RNA Sequencing datasets
We analyzed the transcriptomes of embryonic cortical cells (E15.5 and E17.5) produced by Yuzwa et al. (Yuzwa et al. 2017, GEO: GSE107122.). The Seurat bioinformatic pipeline was used for the analysis as follow. We first created a “Seurat object” including cells expressing more than 100 genes and genes expressed in more than 2 cells. We next performed a Principal Components Analysis (PCA), using the prcomp function of R, after scaling and centering of the data across these genes. This ensured robust identification of the primary structures in the data. We identified statistically significant principal components and used the most significant genes for each of these PCs as input for t-Distributed Stochastic Neighbour Embedding (t-SNE, R package, “perplexity parameter” = 30). To identify clusters, a graph-based clustering approach was used. For the subclustering analysis of E17.5 RGCs only we used the astrocyte marker genes described in Zywitza et al. (Zywitza et al., 2018) as input for t-SNE. All the codes used to produce Figure 11 can be found in Annex 2.
HOPX-Expressing dNSCs Are Biased to Acquire an Astroglial Fate
To confirm a direct relationship between HOPX expression and the generation of distinct neural lineages by dNSCs, we fate-mapped HOPX-expressing NSCs. To this end, we co-electroporated an inducible fluorescent plasmid (pFloxpA-DsRed) with an EGFP plasmid (pCX-GFP) in the dSVZ of P1 HOPXCreert2 mice and analyzed brain sections at 7 and 21 dpe (Figures 11A and 11B). Tamoxifen-mediated activation of the CRE-recombinase in HOPX+ NSCs led to DsRed/GFP co-expression (hereafter termed dsRed) in electroporated cells and their progenies, whilst the HOPX- lineage expressed EGFP only (Figure 11C). Electroporated cell distribution and fate were assessed at both time points on serial sections encompassing the LV and the OB (Figures 11D and 11E). These results revealed the presence of DsRed+ and GFP+ cells at both 7 and 21 dpe (Figure 11F). Remarkably, while the majority of GFP+ cells were found in the OB (7 dpe: 66.2% ± 2.1%;
21 dpe: 77.7% ± 2.5%) and acquired the typical morphology of granule neurons, the majority of DsRed+ cells remained in close proximity to the dSVZ, i.e., in the CC and the cortex (7 dpe: 64.6% ± 5.6%; 21 dpe: 63.8% ± 6.8%) at both time points (Figure 11G). The distinct neural fates adopted by HOPX+ or HOPX- NSCs were examined by immunolabeling of GFAP in DsRed+ and GFP+-expressing progenies in the periventricular regions at 21 dpe. This analysis confirmed that the generation of GFAP+ astrocytes produced by HOPX-expressing NSCs was approximately twice as large compared witth dNSCs that do not express HOPX (59.2% ± 7.1% versus 32.8% ± 4.3%, Figure 11H). These findings confirm HOPX as a marker for a subpopulation of dNSCs primed for astrogenesis.
Table of contents :
1. Regional Heterogeneity and Competence of Neural Stem Cells Throughout Development and Postnatal Life
1.1. The Developing Forebrain Generates Neuronal Diversity
1.1.1 Diversity of the Progenitors during Cortical Neurogenesis
1.1.2 Regionalization of the Developing Brain
1.1.3. Basic Principles of Cortical Organization
1.1.4. Molecular Diversity of Anatomically Defined PNs
1.2. Germinal Activity Persists in the Postnatal SVZ
1.2.1. The Postnatal Niche
1.2.2. The Radial Glia Origin of Type B1 Cells:
1.2.3. Type B1 Cells: A Specialized Astrocyte
1.2.4. Regionalization and heterogeneity of Postnatal SVZ NSCs
1.2.5. Regionalization also applied to gliogenesis
1.2.6. Signals from the CSF and the niche act in concert to regulate NSCs activity and regionalization throughout pre- and postnatal life
1.3. Transcriptional correlates of SVZ NSCs regionalization
1.4. Objectives of the PhD thesis
2. Experimental Chapter 1: HOPX Defines Heterogeneity of Postnatal Subventricular Zone Neural Stem Cells
2.2. Experimental procedures
Hopx Is Enriched in NSCs of the dSVZ and in Cells of the Astrocytic Lineage.
HOPX Expression Reveals Intraregional Heterogeneity within the dSVZ
The dSVZ Is Defined by Microdomains with Distinct Lineage Outputs
HOPX-Expressing dNSCs Are Biased to Acquire an Astroglial Fate
Hopx-expressing RGCs are Present in the Late Developing pallium and Share Transcriptional Featrues with adult Astrocytes
2.5. Supplementary Figures
3. Experimental Chapter 2: Transcriptional Dysregulation in Postnatal Glutamatergic Progenitors Contributes to Closure of the Cortical Neurogenic Period
3.2. Experimental Procedures
Fate Mapping of Birth-Dated Cohorts of Glutamatergic Neurons
A Large Population of Glu Progenitors Persist in the Postnatal SVZ
ScRNA-Seq Reveals Transcriptional Dysregulation in Postnatal Glu Progenitors
Postnatal Glu Progenitor Differentiation Can Be Partially Rescued
3.4. Supplementary Figures
4. General Discussion
4.1. Summary and Opened Questions
4.2. Contributions of Single cell Approaches to Probe Neural Progenitor’s Heterogeneity and Dynamics
4.2.1. Clonal Techniques in Histology and Transcriptomic
4.2.2. Contributions of Single Cell Approaches to Probe Spatial Identity Specification
4.2.3. Contributions of Single cell Approaches to Probe Temporal Identity Specification
4.2.4. Contributions of Single cell Approaches to Probe Adult NSCs Origin and Biology
4.3. Implication for Brain Repair
4.3.1 Are embryonic and Postnatal/adult NSCs Representing Distinct Populations in term of Diversity and Competence?
4.3.2. Targeted Neuronal Ablation as a Model to Study Competence of Postnatal NSCs for Cortical Repair
8. List of Publications
9. Additional Publications