Notch directs telencephalic development and neuron fate determination by regulating miRNA levels
Data files
Apr 05, 2023 version files 4.89 GB
Abstract
The central nervous system (CNS) contains myriads of different types of cells produced from multipotent neural progenitors through multiple rounds of cell divisions. Neural progenitors acquire distinct cell identities depending on their spatial position, but they are also influenced by temporal cues to give rise to different cell populations over time. For instance, the progenitors of the cerebral neocortex generate different populations of excitatory projection neurons following a well-known sequence that is conserved across species. The Notch signaling pathway plays crucial roles in CNS development, including regulating the balance between proliferation and differentiation and fate acquisition. However, the molecular mechanisms by which Notch impacts progenitor fate decisions and telencephalic patterning have not been fully resolved. Here, we show that Notch signaling is essential for neocortical and hippocampal morphogenesis as well as for the development of the corpus callosum and the choroid plexus. In the neocortex, Notch regulates neural progenitor cell cycle dynamics, neurogenesis, and the laminar cytoarchitecture. Our data also indicate that Notch controls projection neuron fate determination through the regulation of two microRNA (miRNA) clusters that include let-7, miR-99a/100, and miR-125b. The expression of competitive inhibitors of these miRNAs rescues the effects of Notch gain-of-function in vivo. Our findings collectively suggest that balanced Notch signaling is crucial for telencephalic development and that the interplay between Notch and miRNAs is critical to control neocortical progenitor behaviors and neuron cell fate decisions.
Methods
FlashTag NSC labelling and FACS
Labelling of cortical neuronal progenitors with carboxyfluorescein esters (CFSEs) was achieved as described elsewhere (Govindan et al., 2018). Briefly, 1μl of a 5mM solution of CellTrace CFSE (from the CellTrace CFSE Cell Proliferation Kit, Invitrogen #C34554) and 0.01% FastGreen in DMSO was injected into the 3rd ventricle of E13.5 control and NICD embryos. Dams were allowed to recover and injected embryos were collected 1 hour post-injection. Embryonic cortices were dissected individually and dissociated into single cells using Papain Dissociation System (Worthington, Cat# LK003150) using manufacturer’s protocol. Cells were resuspended in FACS media (DMEM/F12 without phenol red supplemented with 10% FBS and B-27) and sorted using a Beckman Coulter Astrios EQ Cell Sorter.
RNA and miRNA sequencing
Total RNA from control (n=3) and NICD (n=5) sorted cells was extracted using the Total RNA Purification Plus Kit (NORGEN Biotek Corp., #48300). Gene expression profiling was carried out using a 3'-Tag-RNA-Seq protocol. Barcoded sequencing libraries were prepared using the QuantSeq FWD kit (Lexogen, Vienna, Austria) for multiplexed sequencing according to the recommendations of the manufacturer using both, the UDI-adapterand UMI Second-Strand Synthesis modules (Lexogen). The fragment size distribution of the libraries was verified viamicro- capillary gel electrophoresis on a LabChip GX system (PerkinElmer). Barcoded miRNA-Seq libraries were prepared using the NEXTflex Small RNA Sequencing kit V3(PerkinElmer)with sequence-randomized adapters according to the recommendations of the manufacturer. The fragment size distribution of the libraries was verified via micro-capillary gel electrophoresis on a Bioanalyzer 2100 (Agilent). Both sets of libraries were quantified by fluorometry on a Qubit instrument (Life Technologies, Carlsbad, CA), and pooled in equimolar ratios. The library pools were quantified by qPCR with a Kapa Library Quant kit (Kapa Biosystems/Roche). Finally, both library sets were sequenced on a HiSeq 4000 sequencer (Illumina) with single-end 100 bp reads.
Next Generation Sequencing data processing
For mRNA sequencing: Adapter trimming, quality trimming of the ends of reads, filtering of sequences less than 50 bases, and removal of phiX sequences were conducted using HTStream, version 1.3.3 [1]. Removal of PCR duplicates identified by unique molecular indices (UMI) was conducted using UMI-tools, version 1.0.1 [2]. Reads were aligned to GRCm39 using STAR, version 2.7.3a [3]. Read counts for each gene were obtained using HTSeq, version 0.12.3 [4].
Differential expression analysis was conducted using the limma-voom Bioconductor pipeline [5]. Prior to analysis, genes with fewer than 5 counts per million reads in all samples were filtered prior to analysis, leaving 12,556 genes. Differential expression was defined as a Benjamini-Hochberg [6] adjusted p-value less than 0.05.
For miRNA sequencing: Adapter trimming and filtering of reads with fewer than 14 bases or more than 34 bases was performed using a custom Python script. Removal of phiX sequences was conducted using HTStream, version 1.3.3. Reads were aligned to GRCm39 using STAR, version 2.7.3a, using settings for miRNASeq recommended by the ENCODE project [7,8]. Feature counts were obtained using the featureCounts tool from Subread, version 1.6.3 [9], using settings that allowed multimappers.
Differential expression analysis was conducted using the limma-voom Bioconductor pipeline. Prior to analysis, miRNAs expressed in fewer than 3 samples were filtered, leaving 779 miRNAs. Differential expression was defined as a Benjamini-Hochberg adjusted p-value less than 0.05.
References
1. https://s4hts.github.io/HTStream/
2. Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 2017 Mar;27(3):491-499. doi: 10.1101/gr.209601.116. Epub 2017 Jan 18. PMID: 28100584; PMCID: PMC5340976.
3. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan 1;29(1):15-21. doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25. PMID: 23104886; PMCID: PMC3530905.
4. Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25. PMID: 25260700; PMCID: PMC4287950.
5. Matthew E. Ritchie, Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, Gordon K. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Research, Volume 43, Issue 7, 20 April 2015, Page e47, https://doi.org/10.1093/nar/gkv007
6. Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289--300.
7. https://www.encodeproject.org/
9. Liao Y, Smyth GK and Shi W. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7):923-30, 201
Usage notes
There are two sets of files: 1) Excel spreadsheet files and web browser files can be opened with Excel (Microsoft) software and any web browser; 2) Fastq sequencing files can be open with R. R is available as free software.