DOI: 10.25338/b8rm0h
title: Notch directs telencephalic development and neuron fate determination by regulating miRNA levels

description: 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.
description: <strong>FlashTag NSC labelling and FACS
 </strong> 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.
 <strong>RNA and miRNA sequencing </strong>
 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. <strong>Next Generation Sequencing
 data processing</strong> <strong>For mRNA
 sequencing:</strong> 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. <strong>For miRNA
 sequencing:</strong> 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. <strong>References</strong>
 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/ 8.     https://www.encodeproject.org/documents/b4ec4567-ac4e-4812-b2bd-e1d2df746966/@@download/attachment/ENCODE_miRNA-seq_STAR_parameters_v2.pdf 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
description: 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.