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Quantitative Analysis of Transcriptomes of small molecules generated Neural Precursor Cesll (smNPCs) and smNPCs-derived neurons by next generation sequencing

ABSTRACT: The aim of this next generation sequencing is to analyze the difference between smNPC and smNPC derived neurons by RNA-sequencing. In this study, the smNPCs were generated from inducible pluripotent stem cells (iPSCs) derived from human cord blood cells. The obtained smNPCs were further differentiated into neurons using a protocol that stems from Stuart M Chambers et al (2012, Nature Biotech), which described a system to differentiate human embryonic stem cells into nociceptive sensory neurons and Peter Reinhardt et al (2013, Plos one), which developed a protocol to generate human peripheral neurons from smNPC. Briefly, a peripheral sensory lineage was induced from smNPCs by activating WNT signaling, while inhibiting NOTCH and FGF prior to BMP4 treatment. Neuronal differentiation was further enhanced by treatment with dbcAMP and other growth factors until 60 days post differentiation (dpd). The neurons at 60 dpd are termed hiNeurons. In this study, the transcriptomes of hiNeurons and smNPCs were quantitatively analyzed by RNA-seq.

ORGANISM(S): Homo sapiens

PROVIDER: GSE166442 | GEO | 2022/11/01

REPOSITORIES: GEO

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Project description:Purpose: PDCs will differentiated into different subclusters upon single stimulation. The goals of this study are to identify the heterogeneity of activated pDCs stimulated by R848. Methods: Human primary plasmacytoid dendritic cells were stimulated with R848 for 16 hours. Then, after FACS sorting, about 12000 pDCs were counted and the viability was confirmed to be >90%. Single-cell suspensions were loaded on a 10x Genomics Chromium single-cell 3’ v2 chip to prepare the libraries according to the manufacturer’s protocol. The library quality was checked by Agilent 2100 Bioanalyzer and Qubit fluorometric quantitation. Samples were sequenced on an Illumina NovaSeq 6000 with a sequencing depth of at least 50000 reads per cell. The raw fastq data were processed with CellRanger (v2.1.1) by using GRCh38 annotation (v1.2.0) (Zheng, G.X., et al.Nat Commun 2017;8,14049.). Output from CellRanger was loaded into R and used Seurat (v3.1.1) for further analysis (Stuart, T., et al. Cell 2019;177,1888-1902 e1821.). Cells with fewer than 1000 or more than 4500 expressed genes, and a high percentage of mitochondrial genes (>5%) were removed. We also used cell selection as Peter Karchenko-the Pagoda way to define outliers. Among them, 6010 cells passed the filtering. After normalizing and scaling, we used ‘Find Variable Features’ to identify 1000 highly variable genes. Then we used 992 highly variable genes (removing mitochondrial and ribosomal genes) for PCA. Upon ‘Elbow plot’, we selected the first 20 PC for clustering and UMAP visualization with default parameters. For clustering, we selected resolution parameter=0.2 which produced 4 clusters. To obtain cluster-specific gene signatures, we identified differential expression analysis of each cluster with default parameters. IFN scores based on IFN gene sets were calculated for each cell as the average of scaled expression of these genes and corrected by subtracting the average of a large set of similar genes, as proposed by Tirosh et al (Tirosh, I., et al. Science 2016;352,189-196.). Results: 4 subcluster of pDCs were obtained.Group 0 express chemokines, group 1 express Gzmb, group 2 express type I interferons, and group 3 express Id2 and IL-23A.

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Quantitative Analysis of Transcriptomes of small molecules generated Neural Precursor Cesll (smNPCs) and smNPCs-derived neurons by next generation sequencing

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