ABSTRACT

MicroRNAs (miRNAs) are short (∼22 nt) single-stranded non-coding RNAs that regulate gene expression at the post-transcriptional level. Over recent years, many studies have extensively characterized the involvement of miRNA-mediated regulation in neurogenesis and brain development. However, a comprehensive catalog of cortical miRNAs expressed in a cell-specific manner in progenitor types of the developing mammalian cortex is still missing. Overcoming this limitation, here we exploited a double reporter mouse line previously validated by our group to allow the identification of the transcriptional signature of neurogenic commitment and provide the field with the complete atlas of miRNA expression in proliferating neural stem cells, neurogenic progenitors and newborn neurons during corticogenesis. By extending the currently known list of miRNAs expressed in the mouse brain by over twofold, our study highlights the power of cell type-specific analyses for the detection of transcripts that would otherwise be diluted out when studying bulk tissues. We further exploited our data by predicting putative miRNAs and validated the power of our approach by providing evidence for the involvement of miR-486 in brain development.

INTRODUCTION

MicroRNAs (miRNAs) are short (∼22 nt) single-stranded non-coding RNAs that regulate gene expression at the post-transcriptional level (Lee et al., 1993; Wightman et al., 1993). Canonical miRNAs derive from longer primary transcripts harboring a stem-loop that is processed by two RNAse III enzymes: Drosha in the nucleus and Dicer in the cytoplasm (Kim, 2005; Han et al., 2006). Eventually, mature miRNAs are loaded into the RNA-induced silencing complex (RISC) (Liu et al., 2005; Meister et al., 2005) to destabilize or cleave complementary target messenger RNAs (mRNAs) thereby inhibiting their translation (Bartel, 2004).

miRNA-mediated regulation of translation is far more than an adjustment of cellular protein levels, but rather an essential developmental mechanism. In fact, a number of mouse lines mutant for miRNA-processing enzymes or individual miRNAs showed dramatic phenotypes, ranging from impaired organogenesis to pre- and perinatal lethality (Bernstein et al., 2003; Morita et al., 2007; Wang et al., 2007; Chong et al., 2008). The effects of interfering with miRNA function were found to be particularly severe during brain development, leading to decreased survival of neural progenitors and newborn neurons and ultimately causing cortical malformations (De Pietri Tonelli et al., 2006; Kawase-Koga et al., 2009; Babiarz et al., 2011; Marinaro et al., 2017). In addition, well-established regulatory loops are mediated by miRNAs such as the synergistic effect of miR-9 and let-7b inducing neural progenitor differentiation by targeting the Tlx receptor (Nr2e1) (Zhao et al., 2009, 2010) as well as downregulating Hes1 and cyclin D1 as crucial gene hubs controlling cell-cycle exit and enhancing differentiation (Tan et al., 2012; Ghosh et al., 2014). Moreover, the interaction of miR-9 with miR-124 to target the RE-1 silencing transcript factor (REST), a strong inhibitor of pro-neural genes (Conaco et al., 2006; Visvanathan et al., 2007; Laneve et al., 2010) has been well characterized. Many more examples are known of miRNAs controlling neurogenesis and brain development (Schratt et al., 2006; Fiore et al., 2009; Barca-Mayo and De Pietri Tonelli, 2014; Irie et al., 2016; Leong et al., 2016; Rajman and Schratt, 2017; Jauhari et al., 2018a,b; Nguyen et al., 2018; Nowakowski et al., 2018; Jauhari and Yadav, 2019), highlighting the importance of studying their physiological expression patterns in different cell types of the developing cortex as a crucial step to gain insights into the pathways underlying their timely regulation and function. Remarkably, however, a comprehensive catalog of cortical miRNAs expressed in a cell-specific manner in progenitor types and neurons is still missing.

The lack of a comprehensive catalog of miRNA expression in specific populations of neural progenitor cells is due to many factors, including technical limitations in the coverage of single-cell small RNA sequencing (Faridani et al., 2016) and the fact that essentially all previous high-throughput miRNA studies on neurogenesis used either microarrays or total brain lysates (Krichevsky et al., 2003; Miska et al., 2004; Sempere et al., 2004; Nielsen et al., 2009; Ling et al., 2011). As a consequence, the resolution of previous studies was limited by the variety of probes printed on the microarrays or, alternatively, by the coexistence in time and space of different cell types of the developing brain. To overcome these limitations, we here exploited a previously described dual-reporter mouse line, which allows the isolation of different neural progenitor types and newborn neurons (Aprea et al., 2013).

More specifically, with the progression of neurogenesis two distinct, lineage-related populations of neural progenitors coexist in the developing cortex: radial glia, proliferative progenitors (PPs) that expand the stem cell pool by symmetric divisions, and neurogenic, differentiative progenitors (DPs) that divide to generate neurons (Ns) (Kriegstein and Alvarez-Buylla, 2009; Taverna et al., 2014). In studying the fate and nature of each population, several studies have identified the expression of defined molecular markers in each cell type. In particular, and by taking advantage of Btg2 and Tubb3 expression, our group has generated a combinatorial, double-reporter mouse line in which RFP and/or GFP expression allowed the isolation specifically of PPs, DPs and Ns based on their endogenous fluorescence (RFP−, RFP+ and GFP+, respectively) (Aprea et al., 2013).

Validation and use of this mouse line revealed it to be very powerful in the identification of several new genes and biological processes regulating cortical development (Aprea et al., 2013). This included the thorough characterization of the elusive class of long non-coding (Aprea et al., 2015) and circular (Dori et al., 2019) RNAs, novel transcription factors involved in corticogenesis (Artegiani et al., 2015), and a comprehensive description of DNA methylation and hydroxymethylation as epigenetic marks tuning brain development (Noack et al., 2019).

Given the previously validated power of our approach, we here exploited this Btg2::RFP/Tubb3::GFP line to provide the field with a complete atlas of miRNAs expression in cortical progenitors and neurons of the mouse brain at embryonic day (E) 14.5 as a mid-stage of corticogenesis. Furthermore, and validating our approach, we provide evidence for the involvement of miR-486 as a regulator of corticogenesis.

RESULTS

The comprehensive miRNome of neurogenic commitment

Aiming to profile global miRNA expression during cortical development, we isolated PPs, DPs and Ns (three biological replicates of each) from the lateral cortices of Btg2::RFP/Tubb3::GFP mouse embryos at E14.5, as previously described (Aprea et al., 2013; Dori et al., 2019) (Fig. 1A). Total RNA was used for cDNA library preparation and small RNAs were isolated by size selection, followed by 75-bp high-throughput sequencing. To assemble the catalog of cortical miRNAs, we aligned reads with gsnap (Wu and Nacu, 2010) and used miRBase (v.20) as the most complete reference available to date (Kozomara and Griffiths-Jones, 2014) yielding an average of 1.5 million unique-mapped reads (51% of total). Within the mapped reads, we detected 1058 mature miRNAs (defined as reads ≥1) derived from 703 precursor transcripts (pre-miRNAs) corresponding to 55% and 59% of the 1908 mature and 1186 pre-miRNAs reported in the reference, respectively. More specifically, 640 mature miRNAs were in common to all three cell-types whereas 49 (4.6%), 58 (5.5%) and 129 (12.2%) were specific to PPs, DPs and Ns, respectively (Fig. 1A). Notably, compared with a previous study that reported 294 pre-miRNAs (read count ≥1) expressed in the whole E15.5 mouse brain (Ling et al., 2011), our dataset included essentially all (96%) of these previously known cortical miRNAs and further doubled this list by including an additional 421 pre-miRNAs. This highlights the power of cell type-specific analyses for the detection of transcripts that would otherwise be diluted out when studying bulk tissues.

Fig. 1.

Assembly and validation of the cortical miRNome. (A) Outline of the steps taken to generate the cortical miRNome: sorting of E14.5 PPs, DPs and Ns, followed by small RNA sequencing. Mature miRNAs were identified through alignment on miRBase and novel miRNAs were predicted by miRDeep2. (B) Sagittal sections of E14.5 cortices downloaded from Eurexpress. Magnifications of the lateral cortex are shown to appreciate the extent of the overlap with miRNA expression data measured by deep sequencing (histograms). Data are mean±s.d.; n=3. (C) PCA of DESeq2-normalized 100 most diverse miRNAs between biological replicates (1-3) and cell populations (PPs, gray; DPs, red; Ns, green).

Fig. 1.

Assembly and validation of the cortical miRNome. (A) Outline of the steps taken to generate the cortical miRNome: sorting of E14.5 PPs, DPs and Ns, followed by small RNA sequencing. Mature miRNAs were identified through alignment on miRBase and novel miRNAs were predicted by miRDeep2. (B) Sagittal sections of E14.5 cortices downloaded from Eurexpress. Magnifications of the lateral cortex are shown to appreciate the extent of the overlap with miRNA expression data measured by deep sequencing (histograms). Data are mean±s.d.; n=3. (C) PCA of DESeq2-normalized 100 most diverse miRNAs between biological replicates (1-3) and cell populations (PPs, gray; DPs, red; Ns, green).

Furthermore, given that 42% of our reads did not align to any known miRNA and recent studies reported the detection of novel miRNAs in both mice and humans (Li et al., 2013; de Rie et al., 2017), we hypothesized that some of our reads might derive from miRNAs not yet annotated in any database. We used miRDeep2 (Friedländer et al., 2008) to investigate this possibility. The prediction performed by this tool is based on the putative miRNA primary structure and how reads are aligning to the precursor based on their biogenesis. With this assumption, reads coming from a putative novel miRNA fell into three main categories: the mature sequence, the hairpin loop and the star sequence (22 nt sequence resulting from the removal of the loop that is not loaded into Ago and degraded). If the combination of a possible hairpin precursor and mapping of the sequencing reads did not follow this expected pattern, those reads were discarded. This resulted in the prediction of 163 putative novel miRNAs sequences (read count ≥1), which for convenience were labeled as miR-n- followed by a progressive number as identifier (Fig. 1A, Table S3).

Next, we sought to select and validate some of these predicted miRNAs. To this end, we first chose those showing a higher consistency in detection among biological replicates (i.e. at least two out of three samples from the same cell type) reducing our initial list of 163 to 22 candidates. Next, we rank-ordered this refined cohort of putative novel miRNAs based on their average expression across cell populations selecting the top eight for validation by northern blot with radioactive probes (Table S1). Among these, we confirmed the expression of five showing a size in the range of 90-150 nt (Fig. S1A; note that in some lanes two miR-n are probed with an identical sequence but derived from different loci), which is inconsistent with the known size of either mature or pre-miRNA (20-25 nt and ∼60 nt, respectively) and more in line with that of other small RNAs including t-, sn- or sno-RNAs. Although not excluding the possibility that other novel miRNAs might be present in our list, this exclusion of eight out of eight top-ranking putative novel miRNAs made us conclude that our catalog of mouse cortical miRNAs is virtually complete.

As a next step, we validated the robustness of our datasets following a two-step approach. First, we normalized read numbers using DESeq2 (median-ratio normalization) (Love et al., 2014) to account for differences in sequencing depth. Upon normalization, and according to previous reports (Aprea et al., 2013, 2015; Artegiani et al., 2015; Dori et al., 2019; Noack et al., 2019), principal component analysis (PCA) was performed on the pool of top 100 most variable miRNAs as a more stringent criterion to assess the biological reproducibility of our samples. This showed a clear separation of the three cell types, distributed according to lineage differentiation (PP→DP→N) for the component displaying the highest variance (PC1) (Fig. 1C, Fig. S2A). Second, we selected six miRNAs known to play key roles in neurogenesis and compared their normalized expression measured by deep sequencing with their tissue distribution assessed by in situ hybridization (ISH) data from Eurexpress (Diez-Roux et al., 2011). We observed a nearly perfect overlap between our sequencing and ISH data in all cases, regardless of whether the miRNA was uniformly expressed throughout the cortex (miR-9-5p and miR-17-5p), or enriched in either progenitors (miR-92b-3p, miR-92a-3p and let-7b-5p) or neurons (miR-124-3p) (Fig. 1B, Fig. S1B).

Taken together, our results provide evidence for an overall complete catalog of miRNA expression in cortical cell types during mouse development, more than doubling the previously known list of 292 cortical miRNA precursors (Ling et al., 2011) by detecting 421 additional ones and for a total of 703 transcripts.

Differentially expressed miRNAs

The fine resolution of our system gave us the opportunity to assess differential miRNA expression at the single population level during lineage commitment (Table S4). Therefore, by comparing the PP-DP and DP-N transitions, we identified miRNAs that were up- or downregulated by >1.5-fold (i.e. log2 fold change ≥0.58 or ≤−0.58, respectively; FDR <5%) in one cell type compared with its parental population. As observed previously for linear and circular transcripts (Aprea et al., 2013; Dori et al., 2019), only a small fraction of miRNAs showed a significant change between the PP-DP (7%) and DP-N (17%) transitions; the majority of those up- or downregulated at the PP-DP transition continued to follow the same trend of up- or downregulation, respectively, at the DP-N transition (Fig. 2).

Fig. 2.

Differential expression analysis. Representation of differentially expressed miRNAs in the three cell types (PPs, gray; DPs, red; Ns, green). Numbers indicate the number of miRNA in each group and percentages are calculated over the parental population. miRNAs not detected in PPs or never detected in any cell type are also reported (top left and bottom left, respectively). Oblique lines represent a >50% change (log2 fold change ≥0.58 or ≤−0.58) and FDR <5%, whereas horizontal lines a <50% change or an FDR >5%. Bold lines depict on- and off-switch patterns.

Fig. 2.

Differential expression analysis. Representation of differentially expressed miRNAs in the three cell types (PPs, gray; DPs, red; Ns, green). Numbers indicate the number of miRNA in each group and percentages are calculated over the parental population. miRNAs not detected in PPs or never detected in any cell type are also reported (top left and bottom left, respectively). Oblique lines represent a >50% change (log2 fold change ≥0.58 or ≤−0.58) and FDR <5%, whereas horizontal lines a <50% change or an FDR >5%. Bold lines depict on- and off-switch patterns.

Furthermore, by analyzing coding and long non-coding transcripts, our group previously concluded that a transient up- or downregulation specifically in DPs compared with both their PP progenitors and N progeny (on- and off-switch transcripts, respectively) represented a hallmark of functional commitment to the neurogenic lineage (Aprea et al., 2013; Artegiani et al., 2015). Intriguingly, the subset of miRNAs displaying this on-/off-switch pattern of expression was strongly under-represented, accounting for only 0.5% of the total and suggestive of a highly specific expression pattern. In fact, we only found two on-switch (let-7b-5p and miR-135a-2-3p) and two off-switch (miR-486a-5p and miR-486b-5p) miRNAs (Figs 2 and 3). Supporting our conclusion that on-/off-switch transcripts are functionally involved in neurogenic commitment, both let-7b and miR-135a-2 are well known to be key regulators of neurogenesis (Zhao et al., 2010; Caronia-Brown et al., 2016). In contrast, although it has been shown that miR-486a and miR-486b promote myoblast differentiation (Dey et al., 2011) and are involved in regulatory pathways of ectodermal-derived tissues (Jee et al., 2012; Kurtenbach et al., 2017), no neurogenesis-related function has ever been reported for these two miRNAs to date.

Fig. 3.

Genomic location of switch miRNAs. Right: Genes hosting switch miRNAs are depicted (black); blue arrows represent the direction of transcription, whereas black boxes and lines constitute exons and introns, respectively. The position and mature sequence of switch miRNAs are indicated in red. Left: Expression patterns of miRNAs and host genes. Data are mean±s.d.; n=3.

Fig. 3.

Genomic location of switch miRNAs. Right: Genes hosting switch miRNAs are depicted (black); blue arrows represent the direction of transcription, whereas black boxes and lines constitute exons and introns, respectively. The position and mature sequence of switch miRNAs are indicated in red. Left: Expression patterns of miRNAs and host genes. Data are mean±s.d.; n=3.

With regard to their genomic location, we observed that all four switch miRNAs were intragenic. In particular, let-7b-5p and miR-135a-2-3p were processed, respectively, from lncRNAs AC162302.2 and Rmst (which mediates Sox2-dependent progenitor proliferation) (Ng et al., 2013). Similarly, miR-486a-5p and miR-486b-5p were processed from ankyrin 1 (Ank1) and the predicted gene Gm15816, respectively. Interestingly, miR-486a-5p and miR-486b-5p shared the same mature sequence, despite originating from different pre-miRNAs transcribed from opposite strands of the same genomic locus. Unsurprisingly, when analyzing the expression pattern of the host genes of switch miRNAs (data retrieved from Aprea et al., 2013), we found a high degree of overlap in their differential expression within different cell populations (Fig. 3). This was consistent with previous observations of our group on long non-coding and circular RNAs (Aprea et al., 2015; Dori et al., 2019) in which intragenic, switch genes were found to be regulated together in a similar fashion at the level of a common ‘switch locus’.

The off-switch miR-486a/b-5p is a regulator of neurogenesis

The absence of any known function for miR-486a/b-5p in neural stem cells or brain development, together with their intriguing switch expression pattern, drove us to investigate their potential functional role during corticogenesis while also attempting to validate the power of our miRNome atlas of cortical cell types. After validating the off-switch expression pattern of miR486a/b-5p by qRT-PCR on fluorescence-activated cell (FAC)-sorted PPs, DPs and Ns of the E14.5 cortex (Fig. S2B), we designed locked nucleic acids (LNAs) to inhibit the activity of this miRNA, confirming their efficacy by luciferase assay on two of its validated targets: Foxo1 and Pten (Small et al., 2010; Wang et al., 2015) (see supplementary Materials and Methods and Fig. S2C,D). Next, to investigate the effect of miR-486a/b-5p inhibition on cortical progenitors, we used in utero electroporation at E13.5 as a means to deliver LNA-486 to apical, radial glia cells, which at this stage of development are mostly PPs [60-70% (Aprea et al., 2013)] and as a means to prematurely inhibit the activity of this off-switch miRNA in this cell population. Brains were collected 48 h later and distribution of electroporated cells of LNA-486 and an LNA-control (both identified as RFP+) compared as a readout of neurogenesis and neuronal migration.

LNA-486 significantly altered cell distribution across all cortical layers. Particularly affected were the subventricular zone and the cortical plate, which showed a 1.6-fold increase (from 12±1 to 20±2%; P<0.01), and a comparable decrease (from 20±3 to 12±2%; P<0.01), in RFP+ cells after delivery of LNA-486 relative to brains electroporated with control LNAs, respectively (Fig. 4A). By using Tbr2 (also known as Eomes) as a marker to discriminate basal from apical progenitors, we observed a significant increase in both cell types at the expense of neurons. In particular, apical progenitors increased by 1.2-fold (from 14±0.2 to 18±2%; P<0.05), whereas basal progenitors increased by 1.3-fold (from 21±2 to 28±4%; P<0.05). This was paralleled by a comparable decrease in neurons found in the neuronal layers (from 66±2 to 54±2%; P<0.001) (Fig. 4B). Notably, no major effect was found either at the level of cell survival or migration of newborn neurons as assessed by activated-caspase immunoreactivity or upon 24 h birthdating with bromodeoxyuridine (Fig. S3A,B) and hinting at a cell fate-specific effect upon inhibition of miR-486a/b-5p activity.

Fig. 4.

In vivo manipulation of miR-486a/b-5p. (A,B) Coronal sections of electroporated lateral cortices stained for RFP (electroporated cells, red), DAPI (all nuclei, blue) (A) and Tbr2 (basal progenitors, green) (B). Histograms represent quantifications of cell distribution 48 h after electroporation of LNA-control (white) or LNA-486 (black). AP, apical progenitors (Tbr2− cells in VZ); BP, basal progenitors (Tbr2+ cells in VZ and SVZ); CP, cortical plate; IZ, intermediate zone; SVZ, sub-ventricular zone; VZ, ventricular zone. Data are mean±s.d.; n=3 (LNA-Control); n=4 (LNA-486). Individual dots represent biological replicates. *P<0.05; **P<0.01; ***P<0.001. Scale bars: 25 µm.

Fig. 4.

In vivo manipulation of miR-486a/b-5p. (A,B) Coronal sections of electroporated lateral cortices stained for RFP (electroporated cells, red), DAPI (all nuclei, blue) (A) and Tbr2 (basal progenitors, green) (B). Histograms represent quantifications of cell distribution 48 h after electroporation of LNA-control (white) or LNA-486 (black). AP, apical progenitors (Tbr2− cells in VZ); BP, basal progenitors (Tbr2+ cells in VZ and SVZ); CP, cortical plate; IZ, intermediate zone; SVZ, sub-ventricular zone; VZ, ventricular zone. Data are mean±s.d.; n=3 (LNA-Control); n=4 (LNA-486). Individual dots represent biological replicates. *P<0.05; **P<0.01; ***P<0.001. Scale bars: 25 µm.

Taken together, characterization of the miRNome of progenitor cell types and neurons revealed a powerful tool to identify new miRNAs involved in cortical development allowing us to describe for the first time the functional effects of interfering with the activity of miR-486a/b.

DISCUSSION

Here, we provide a complete catalog of miRNA expression in neural progenitors and newborn neurons during cortical development and validate our resource by identifying a new player in neural stem cell fate specification: the switch miR-486a/b-5p.

Since the discovery of miRNAs as crucial regulators of translation (Bartel, 2004), many groups have attempted to obtain atlases of their expression during brain development. The use of microarrays offered a first approach toward this goal (Krichevsky et al., 2003; Miska et al., 2004; Sempere et al., 2004; Nielsen et al., 2009) but was limited by a previous knowledge about the sequence of such miRNAs. Next-generation sequencing overcame this limitation and significantly increased the number of known miRNAs (Ling et al., 2011). However, previous studies were limited either to the use of cell cultures or to analyses of whole brain lysates owing to a lack of systems to discriminate between different cellular subtypes coexisting in time and space during corticogenesis. Even with the advent of single-cell sequencing, the study of small RNAs remains hindered by two major technical limitations: (1) except for analyses limited to the top most expressed miRNAs (Xiao et al., 2018; Wang et al., 2019), drop-seq is currently applicable only to poly(A)-RNAs and (2) library preps with <1000 cells display extremely poor coverage (Faridani et al., 2016).

Here, we exploited the Btg2::RFP/Tubb3::GFP mouse line as a well-established tool used by our group in previous studies to characterize the molecular signature of neurogenic commitment (Aprea et al., 2015; Artegiani et al., 2015; Dori et al., 2019; Noack et al., 2019). By doing so, our group identified switch transcripts belonging to several classes of RNAs and including coding, long non-coding and circular RNAs and in most cases showing their functional roles in brain development (Aprea et al., 2013; Artegiani et al., 2015; Dori et al., 2019). Continuing this line of research, not only have we provided the field with a validated and overall complete catalog of cortical miRNAs of individual cell types but we have also identified miR-486a/b-5p as a regulator of neurogenesis. We hope that future studies will be able to dissect the molecular mechanisms underlying this cortical, switch miRNA and that the field in general will profit from this resource.

MATERIALS AND METHODS

Animals and embryo dissection

Mice were housed into the Biomedical Services Facility (BMS) of the MPI-CBG under standard conditions (12-h light-dark cycle, 22±2°C temperature, 55±10% humidity, food and water supplied ab libitum). All experimental procedures were performed according to local regulations and all animal experiments were approved by local authorities (Landesdirektion Sachsen; 24D-9168.11-1/41, 2008-16, 2011-11, TVV 39/2015, 13/2016 TVV and 16-2018). Btg2RFP/Tubb3GFP males were time-mated with C57BL/6J females, which were marked as E0.5 the morning that a spermatic plug was observed. Pregnant females were anesthetized using isoflurane (Baxter) and sacrificed by cervical dislocation at E14.5. Brains of RFP/GFP double-positive embryos were collected and lateral cortices isolated after removal of meninges and ganglionic eminences. Plugged C57BL/6J females for in utero electroporation or RNA extraction for northern blot were purchased from Janvier Labs. Mice were sacrificed at E14.5 or E15.5 and embryo brains and cortices dissected as above.

Cell dissociation and FAC sorting

Lateral cortices of RFP/GFP double-positive embryos were dissociated using Papain-based Neural Tissue Dissociation Kit (Miltenyi Biotech) according to the manufacturer's protocol. Cells were re-suspended in 1 ml of ice-cold PBS and 10 µl of 7-AAD (BD Pharmingen) were added for dead cell discrimination. Sorting was performed using a BD FACSAria III (BD Biosciences) with previously described gating (Aprea et al., 2013; Dori et al., 2019). A minimum of 1×106 cells per sample was collected in PBS and centrifuged (300 g, 10 min at 4°C) before RNA extraction.

RNA extraction

For miRNA deep sequencing, total RNA was isolated using Quick RNA Mini Prep kit (Zymo Research) from cells sorted as described above. RNA quality and integrity were assessed using a Bioanalyzer (Agilent Genomics). RNA integrity (RIN) values were above 9.0. For northern blots, total RNA was isolated by TRI Reagent (Sigma-Aldrich). Briefly, lateral cortices of all E14.5 embryos of one litter were pooled and lysed in 1 ml of TRI Reagent. Samples were added to 200 µl of chloroform, mixed and left at room temperature (RT) for 15 min before centrifugation at 12,000 g for 30 min at 4°C. Aqueous phases were transferred to new tubes and RNAs were precipitated by adding 500 µl of 2-propanol. RNA pellets were washed with 1 ml of 75% ethanol and eventually re-suspended in 50 µl of nuclease-free water.

Library preparation and small RNA deep sequencing

Library preparation was performed on 1 µg of total RNA with the NEB Next Small RNA Library Prep Kit. All cDNA libraries were prepared according to the manufacturer's specifications, including adapter ligation, first-strand cDNA synthesis, PCR enrichment and size selection. cDNA purity and concentration after gel extraction were measured by qPCR. Samples were sequenced on Illumina HISeq 2500 and single-end 75-bp reads were obtained.

Bioinformatics and statistical analyses

Sequencing data were obtained for PPs, DPs and Ns in three biological replicates. After adapter removal, reads shorter than 30 bp were aligned to miRBase v.20 (Kozomara and Griffiths-Jones, 2014) using gsnap (Wu and Nacu, 2010). Alignment was performed in three consecutive steps: (1) alignment on mature miRNA sequences, (2) extraction of unmapped reads and (3) alignment on precursor-miRNA. During all steps, no mismatches were allowed and multi-mapped reads were discarded. Eventually, a table of read counts per mature miRNA (read count≥1) was assembled. For novel miRNA prediction, all unmapped reads were extracted and aligned using miRDeep2 (Friedländer et al., 2008) on mouse genome (mm10). The R package DESeq2 (Love et al., 2014) was used for normalization of the read count table and further testing of differential expression. Mean counts from replicates were used for fold change (FC) calculations: log2FC values ≥0.58 or ≤−0.58 were considered up- or downregulation, respectively. Benjamini–Hochberg procedure was applied for multiple t-test adjustment and FDR values lower than 0.05 were considered significant. A minimum of three biological replicates was used for any other assessment presented (considering a biological replicate as individual embryo from different litters). Statistical differences of mean values were calculated by two-tailed Student's t-test, with P<0.05 considered significant.

In situ hybridization

In situ hybridization was performed as previously described (Laguesse et al., 2015), using digoxigenin (DIG)-labeled LNA probes purchased from Exiqon (sequences are listed in Table S1). Cryosections were fixed in 4% paraformaldehyde (PFA) for 10 min and acetylated (100 mM triethanolamine, 0.25% acetic anhydride) for 15 min with constant rocking at RT. Hybridization was performed in hybridization buffer (HB) containing 50 nM LNA probes (previously denatured at 75°C for 5 min) overnight at 53°C. After washing (see supplementary Materials and Methods for extended protocol and all pre-hybridization and washing steps), blocking solution (MABT, 20% goat serum, 2% Boehringer Blocking Reagent) was applied for 30 min at RT, followed by incubation with anti-DIG-AP antibody (1:2000 in blocking solution, Roche) overnight at 4°C. Labeling was developed in BM purple (Roche) overnight at 37°C. Sections were imaged using an automated microscope (ApoTome; Carl Zeiss), pictures digitally assembled using Zen software (Carl Zeiss) and composites analyzed using Photoshop CS6 (Adobe).

Northern blot

Total RNA (30 µg) extracted from E14.5 mouse cortices was separated using denaturing urea 15% PAGE gel (Mini-PROTEAN system; Bio-Rad) in 1× TBE and blotted onto a GeneScreen Plus nylon membrane (PerkinElmer) in pre-cooled 0.5× TBE. Radioactively labeled Decade marker (Ambion) was used as molecular marker. After UV-crosslinking, membranes were pre-incubated in hybridization buffer at 50°C in constant rotation and then incubated overnight at 50°C in hybridization buffer containing the denatured [32P]-labeled DNA probes against the predicted novel mature miRNA sequences (see supplementary Materials and Methods for additional details on cross-linking and buffers). Probes against miR-9-5p and miR-124-3p were used as positive controls (see Table S1 for the complete list of probes sequences). Signals were detected by autoradiography using the Cyclone Plus Phosphor Imager (Perkin Elmer). The membrane was stripped (0.1% SDS, 5 mM Na-EDTA, preheated to 95°C) for 1 h and re-used several times to detect additional miRNAs.

In utero electroporation

LNA oligonucleotides (miRCURY LNA miRNA Inhibitors) were purchased from Exiqon and co-electroporated with pDSV-mRFPnls reporter plasmid (Lange et al., 2009). LNA sequences are reported in Table S1. In utero electroporation was performed as previously described (Lange et al., 2009; Artegiani et al., 2011): C57BL/6J pregnant mice were anesthetized with isoflurane at E13.5 and 1 µl of DNA solution (10 µM LNA, 0.8 µM RFP plasmid) was injected into the left ventricle of the embryo, followed by the application of six electric pulses (30 V and 50 ms each at 1 s intervals) through platinum electrodes using a BTX-830 electroporator (Genetronics). Embryos were collected 48 h post electroporation (E15.5) (see supplementary Materials and Methods)

Cloning and luciferase assay

psiCHECK-2 double luciferase vector containing human PTEN 3′-UTR flanked by XhoI and NotI restriction sites was purchased from Addgene (plasmid #50936). Human PTEN 3′-UTR was replaced by parts of the 3′-UTRs of Foxo1 and Pten, which were PCR amplified from mouse genomic DNA (kindly provided by Sara Zocher, Kempermann G. lab DZNE, German Center for Neurodegenerative Diseases within the Helmholtz Association, Dresden, Germany), inserted into the psiCHECK-2 vector downstream of Renilla luciferase (see supplementary Materials and Methods for more details and Table S1 for the list of cloning primer sequences) and a 3-nt-mutation in miR-486-5p binding site were introduced. pre-miR-486a (miRBase accession number: MI0003493) plus 50 bp flanking each side were PCR-amplified from mouse genomic DNA (kindly provided by Sara Zocher, Gerd Kemperman's Lab) and cloned downstream of the U6 promoter of the pSilencer2.1-U6-Neo vector (Thermo Fisher). Additionally, a nuclear-localized RFP protein with its SV40 promoter (from pDSV-RFPnls plasmid; Lange et al., 2009) was added into the same vector replacing the Neomycin cassette (see supplementary Materials and Methods for additional information and Table S1 for primer sequences). All sequences (and introduced mutations) were confirmed by Sanger sequencing. Luciferase assays were performed using Neuro2A (N2A, these cells were chosen because they lack expression of miR-486a/b). Cells were co-transfected with psiCHECK-2, miR-486a pSilencer 2.1 and LNA (either LNA-control or LNA-486; more details are provided in supplementary Materials and Methods) and 24 h post-transfection cells were washed with PBS, lysed and a luciferase assay was performed using Dual Luciferase Reporter Assay System according to manufacturer protocol (Promega).

Immunohistochemistry

After dissection, brains were fixed in 4% PFA in 0.1 M phosphate buffer (pH 7.4) overnight at 4°C, cryoprotected in 30% sucrose and cryosectioned (10 µm thick slices) using a Microm HM 560 Cryostat (ThermoFisher Scientific). Immunohistochemistry was performed as previously described (Lange et al., 2009) (see supplementary Materials and Methods for more details and Table S2 for a list of antibodies used). Nuclei were counterstained with DAPI. Sections were imaged using an automated microscope (ApoTome; Carl Zeiss). Images were digitally assembled using Axiovision software (Carl Zeiss) and composites analyzed using Photoshop CS6 (Adobe).

Acknowledgements

We thank the MPI-CBG and CRTD facilities for maintenance of the mouse lines, sequencing and FACS. We would also like to thank the Huttner Lab (MPI-CBG) for providing N2A cells and Sara Zocher (Kempermann Lab, DZNE) for providing mouse genomic DNA for cloning purposes.

Footnotes

Author contributions

Conceptualization: M.D., D.C., F.C.; Methodology: M.D., D.C.; Validation: M.D., D.C., S.M., L.H.A.A., B.C.d.T.; Formal analysis: M.D., D.C., M.L.; Investigation: M.D., D.C., S.M., L.H.A.A., B.C.d.T., S.K., G.S.; Data curation: M.D., D.C., M.L., A.D., F.C.; Writing - original draft: M.D., D.C., F.C.; Writing - review & editing: M.D., D.C., F.C.; Supervision: F.C.; Project administration: F.C.; Funding acquisition: F.C.

Funding

This work was supported by the CRTD (Zentrum für Regenerative Therapien Dresden), Technische Universität Dresden, Deutsche Forschungsgemeinschaft (CA893/9-1), a DIGS-BB (Dresden International Graduate School for Biomedicine and Bioengineering) fellowship to M.D. and D.C. and by the Italian Epigenomics Flagship Project (Epigen) of the Ministero dell'Istruzione, dell'Università e della Ricerca.

Data availability

Sequencing data generated during the current study have been deposited in the Gene Expression Omnibus repository under accession number GSE142253.

Peer review history

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Competing interests

The authors declare no competing or financial interests.

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