Project description:Olfactory ensheathing cells are one of the few central nervous system regenerative cells discovered so far. It is characterized by its lifelong nerve regeneration function, and it can also release a variety of neurotrophic factors and neural adhesion molecules. It is considered to be the glial cell with the strongest myelination ability. Olfactory ensheathing cells and Schwann cells have phenotypes in common, they can promote axon regeneration(R. Doucette, 1995). Olfactory ensheathing cells have the characteristics of Schwann cells and astrocytes, but the overall performance tends to be the former, which has two unique characteristics. First, it exists not only in the peripheral nerves (Schwann cells), but also in the central nervous system (astroglia); second, the olfactory mucosa has the ability to regenerate life-long, including human olfactory ensheathing cells(J. C. Bartolomei and C. A. Greer, 2000). Regeneration is a process in which olfactory ensheathing cells participate in efficient regulation, although the specific mechanism is not yet clear. Olfactory ensheathing cells are different from astrocytes and Schwann cells, but at the same time have the characteristics of these two cells(S. C. Barnett, 2004), like Schwann cells help axon growth, but more than Schwann cells It can make axons grow long distances, that is, it has stronger migration(A. Ramon-Cueto et al., 1998); there are also astrocytes that have a nutritional effect on the survival of neurons and the growth of axons, but olfactory ensheathing cells can also wrap neurons forms myelin sheath to support the growth of nerve processes(R. Devon and R. Doucette, 1992; J. Gu et al., 2019). There are two characteristics that make olfactory ensheathing cells the best choice for the treatment of neurological diseases(S. C. Chiu et al., 2009; J. Kim et al., 2018; M. Abdel-Rahman et al., 2018). Olfactory ensheathing cells are gradually used to treat spinal cord injuries and have shown amazing effects(J. C. Bartolomei and C. A. Greer, 2000; K. J. Liu et al., 2010; R. Yao et al., 2018). Olfactory ensheathing cells that have been used in research are usually derived from the olfactory bulb(E. H. Franssen et al., 2007), but it is easier to obtain olfactory ensheathing cells from the olfactory mucosa in clinical practice(M. Ryszard et al., 2006), so the difference between the olfactory ensheathing cells from the olfactory bulb and the olfactory mucosa There are more and more studies(B. M. U. et al., 2007), and previous studies have shown that they not only have many similar functions, but also have many differences(M. W. Richter et al., 2005; L. Wang et al., 2014; K. E. Smith et al., 2020). Because olfactory ensheathing cells derived from the olfactory bulb are not easy to obtain, olfactory ensheathing cells derived from the olfactory mucosa have become the focus of attention. Although we know that olfactory ensheathing cells from two sources have nerve repair functions, it is not clear why the two different sources of olfactory ensheathing cells have different therapeutic effects. Nicolas G. once studied that the genetic difference between the two cells and found that there are many genes related to wound repair and nerve regeneration(G. Nicolas et al., 2010). We have reason to guess that olfactory ensheathing cells from these two sources will also have a large difference in protein level. Our research group wants to use the current mature transcriptome and proteomic sequencing technologies to explore the difference between olfactory ensheathing cells from the olfactory bulb and olfactory mucosa, and explain why the two sources of olfactory ensheathing cells shows different therapeutic effects, hope to provide a new theoretical basis for future clinical treatment.

Project description:We generated C57BL/6 mice lacking Bmp10 and/or Bmp9 utilizing the Cre-loxP system. Briefly, Bmp9 constitutive deletion resulted from the replacement of exon 2 by a neomycin resistance cassette. Because Bmp10 deletion leads to early embryonic lethality, we used the tamoxifen-inducible Cre system to generate Bmp10-cKO mice (Rosa26-CreERT2;Bmp10lox/lox) by crossing Rosa26-CreERT2 mice with Bmp10lox/lox mice that possess loxP sites flanking exon 2. To generate double-KO (DKO) mice, we crossed these Rosa26-CreERT2;Bmp10lox/lox mice with Bmp9-KO mice. At the age of 8 weeks, mice were treated with tamoxifen (Sigma) by intraperitoneal injection once a day for 5 days at a dosage of 50 mg/kg. At the age of 5 months, Wild Type and DKO mouse lung tissue was flash frozen in liquid nitrogen and stored at -80°C. RNA extraction, RNA sample quality assessment, RNA library preparation, sequencing and raw data analysis were conducted at GENEWIZ, Inc. (South Plainfield, NJ, USA). Total RNA was extracted from frozen tissue using the Qiagen RNeasy Plus Mini kit. RNA samples were quantified using Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and RNA integrity was checked with Agilent TapeStation (Agilent Technologies, Palo Alto, CA, USA). rRNA depletion was performed using Ribozero rRNA Removal Kit (Illumina, San Diego, CA, USA). RNA sequencing library preparation used NEBNext Ultra RNA Library Prep Kit for Illumina by following the manufacturer’s recommendations (NEB, Ipswich, MA, USA). Briefly, enriched RNAs were fragmented for 15 minutes at 94 °C. First strand and second strand cDNA were subsequently synthesized. cDNA fragments were end repaired and adenylated at 3’ends, and universal adapter was ligated to cDNA fragments, followed by index addition and library enrichment with limited cycle PCR. Sequencing libraries were validated using the Agilent Tapestation 4200 (Agilent Technologies, Palo Alto, CA, USA), and quantified by using Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) as well as by quantitative PCR (Applied Biosystems, Carlsbad, CA, USA). The sequencing libraries were clustered on one lane of a flowcell. After clustering, the flowcell was loaded on the Illumina HiSeq 4000 instrument (or equivalent) according to manufacturer’s instructions. The samples were sequenced using a 2x150 Paired End (PE) configuration. Image analysis and base calling were conducted by the HiSeq Control Software (HCS). Raw sequence data (.bcl files) generated from Illumina HiSeq was converted into fastq files and de-multiplexed using Illumina's bcl2fastq 2.17 software. One mis-match was allowed for index sequence identification. After investigating the quality of the raw data, sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality using Trimmomatic v.0.36. The trimmed reads were mapped to the the Mus musculus GRCm38 reference genome available on ENSEMBL using the STAR aligner v.2.5.2b. Gene counts were calculated from uniquely mapped reads using feature Counts from the Subread package v.1.5.2. Only unique reads that fell within exon regions were counted. The gene hit counts table was then used for downstream differential expression analysis. A differential gene expression analysis between WT and DKO groups of samples was performed using the R-package DESeq2 (Wald test).

Project description:Medulloblastoma is subdivided into different subgroups: WNt, SHH, Group 3 and Group 4. Since these subgroups are associated with different OS and metastasis rates it is crucial to understand them better. Six medulloblastoma cell lines, DAOY, ONS-76, D458, HD-MB03, CHLA-01-MED, CHLA-01R-MED, have been sequenced to compare them with medulloblastoma patient data. Methods: Medulloblastoma cell lines representating the different subgroups have been cultured and cell were harvested and RNA was isolated when 70% confluency was reached. In detail, DAOY and D458 were grown in DMEM (Thermo Fisher) with 10% FBS (HyClone, Thermo Fisher), ONS-76 and HD-MB03 were grown in RPMI 1640 (Sigma-Aldrich) with 10% FBS and CHLA-01-MED and CHLA-01R-MED were grown in DMEMF12 supplemented with B27, 20 ng/ml EGF and 20 ng/ml bFGF (all Thermo Fisher). All cells were maintained at 37 °C in a humidified atmosphere containing 5% CO2. During the course of this study, all cell lines were routinely confirmed to be mycoplasma negative (MycoAlert, Lonza, Basel, Switzerland).Cell pellets of at least 100,000 cells were washed with HBSS and frozen in liquid nitrogen. For homogenization, ceramic spheres (Lysing Matrix D, MP Biomedicals, Santa Ana, California, USA) and the FastPrep-24 homogenizer was used (MP Biomedicals, speed 4 m/s, tube holder MP:24*2 and time 20 s). Total RNA was isolated from 2D pellets using the NucleoSpin RNA Plus Kit (Macherey-Nagel, Düren, Germany) according to the manufacturer’s instructions. In total, 3 biological replicates of each cell line were processed respectively. RNA amount was determined using the Qubit RNA BR kit with the Qubit 4 (both ThermoFisher). Library preparation and RNA sequencing (transcriptome sequencing including lncRNA on Illumina PE150) were performed by Novogene (Cambridge, UK) Company Limited, Cambridge, UK. Samples with less than 100 ng or with non-qualifying RIN values were excluded from the sequencing. All prepared libraries successfully passed Novogene’s internal quality control checks and were sequenced. Following sequencing, quality control of the sequencing data was performed that confirmed all samples had high quality scores, indicating good technical performance of the sequencing. We used FastQC to perform quality checks of raw RNA data followed by adapter and low quality read filtering using the Cutadapt package (version 1.16.6) [reference]. The trimmed paired-end sequences were aligned with the human genome (hg38) and Gencode annotation (v35) using the STAR (version 2.7.5b) alignment tool. Unique reads from genomic alignment were processed and we used the featureCount tool for transcript abundance quantification. STAR read counts were used as input into edgeR. Genes with read counts greater than 10 in three or more samples were kept for subsequent analyses. After normalization analyses, counts per million (cpm) on a log2 scale were used for downstream exploratory analyses.

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RNA-seq of NEAT1 knockdown in neuronal excitability

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