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| Status |
Public on Dec 31, 2022 |
| Title |
lung-Herts/33- |
| Sample type |
SRA |
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| Source name |
lung
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| Organism |
Gallus gallus |
| Characteristics |
tissue: lung
cell type: CD45-
treatment: Herts/33
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| Extracted molecule |
total RNA |
| Extraction protocol |
Infected or PBS-treated chickens were sacrificed by spinal cord injury. Their lungs were subsequently perfused with PBS through the right ventricle, and were divided into two groups that were subjected to different dissociation techniques. Half of the entire pool of lungs was dissected and dissociated into single-cell suspensions using the Multi Tissue Dissociation Kit 1 (Miltenyi Biotec) in combination with a gentle magnetic activated cell sorting (MACS) dissociator (Miltenyi Biotec) and enzymatic dissociation. The other half of the lung pool was minced on ice to <1-mm cubic pieces, suspended in 5 mL of digestion buffer consisting of elastase (3 U/mL; Worthington Biochemical Corporation) and DNase I (0.33 U/mL; Sigma-Aldrich) in DMEM/F12 medium, incubated with frequent agitation at 37°C for 20 min and briefly triturated(Treutlein et al., 2014). Next, an equal volume mixture of DMEM/F12 supplemented with 10% fetal bovine serum (FBS) and penicillin–streptomycin (1 U/mL, Biological Industries) was added to the single-cell suspensions in both groups. Following enzymatic incubation, cells derived from the same lungs (after the two dissociation techniques) were passed through a 100-μm sieve and then through a 70-μm sieve, pelleted again (300 g, 5 min, 4°C), and resuspended in MACS buffer (0.5% bovine serum albumin, 2 mM EDTA in PBS). Red blood cells were removed by depletion with chicken red blood antibody (Fitzgerald Industries International, Inc.) against fluorescein isothiocyanate conjugated to magnetic beads (Miltenyi Biotec). Cell populations were sorted using chicken CD45 antibody (SouthernBiotech) against biotin conjugated to magnetic beads (Miltenyi Biotec).Chicken embryo fibroblast cell lines DF-1 (ATCC, CRL-12203) were seeded in a 15-cm plate and were cultured overnight. When the cell density reached approximately 80% confluence, cells were infected with the highly virulent Herts/33 strain at a multiplicity of infection (MOI) of 1 and were incubated at 37°C with 5% CO2 for 1 h. The growth medium was replaced with DMEM supplemented with 2% FBS and was incubated for 12 h before harvesting. There was an obvious cytopathic effect on DF-1, which confirmed NDV infection. Uninfected cells were considered negative controls. Single-cell suspensions were prepared based on the 10x Genomics protocols.
Cells isolated from each group were captured in droplet emulsions using the 10x Genomics chromium single-cell instrument, and libraries were prepared using the 10x Genomics 3′ Single Cell v2 protocol for the DF-1 cell line group and the 10x Genomics 3′ Single Cell v3 protocol for the chicken group as previously described. All 10x libraries were pooled and sequenced on the NovaSeq 6000 platform (Illumina).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
10x Genomics
Herts/33-CD45-
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| Data processing |
The raw deep sequence data were processed using the 10x Genomics software package CellRanger (v5.0.0). The reads were aligned to a concatenation of the chicken reference genome (GRCg6a) and Newcastle disease virus (NDV) genome. The complete genome sequence of the highly virulent Herts/33 strain was from GenBank AY741404.1, and that of the lentogenic strain LaSota was from GenBank JF950510.1. The chicken transcriptome was generated by filtering genome assembly GRCg6a for protein-coding genes defined in the GTF file. We processed the unique molecular identifier (UMI) count matrix using the Seurat (v3.1.1) R package(Butler et al., 2018). To remove low-quality cells and likely multiplet captures—a major concern in microdroplet-based experiments, we applied a criterion to filter out cells with UMI/gene number of the limit of mean value +/− two-fold standard deviations with a Gaussian distribution of the UMI/gene numbers in each cell. Following visual inspection of the distribution of cells by the fraction of mitochondrial genes expressed, we further discarded low-quality cells where >30% of the counts belonged to mitochondrial genes. In addition, we used the DoubletFinder package (v2.0.2)(McGinnis et al., 2019) to identify potential doublets. After applying these quality control criteria, 91,447 single cells were included in downstream analyses. Library size normalization was performed with the NormalizeData function in Seurat to obtain the normalized count. Specifically, the global-scaling normalization method “LogNormalize” was used to normalize the gene expression measurements for each cell by the total expression, multiplied by a scaling factor (10,000 by default), and the results were log-transformed. Top variable genes across single cells were identified using the method described in Macosko et al.(Macosko et al., 2015). The most variable genes were selected using FindVariableGenes function (mean.function = FastExpMean, dispersion.function = FastLogVMR) in Seurat. To remove the batch effects in scRNA-seq data, the mutual nearest neighbors method by Marioni et al. was performed with the batchelor R package(Haghverdi et al., 2018). Graph-based clustering was performed to cluster cells according to their gene expression profile using the FindClusters function in Seurat. Cells were visualized using a two-dimensional t-distributed stochastic neighbor embedding (t-SNE) algorithm with the RunTSNE function in Seurat. We used the FindAllMarkers function (test.use = bimod) in Seurat to identify marker genes of each cluster. For a given cluster, FindAllMarkers identified positive markers compared with all other cells.
Clustering results for individual or grouped samples were visualized using t-SNE. Cell types were classified based on differential expression analysis, with cluster-specific marker genes identified by the FindMarkers function. GO enrichment analysis of cell type-specific marker genes was performed with ToppGene online tools.
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| Submission date |
May 03, 2022 |
| Last update date |
Dec 31, 2022 |
| Contact name |
Chan Ding |
| Organization name |
Shanghai Veterinary Research Institute,Chinese Academy of Agricultural Sciences
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| Street address |
No.518 Ziyue Road
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| City |
Shanghai |
| ZIP/Postal code |
200241 |
| Country |
China |
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| Platform ID |
GPL26853 |
| Series (1) |
| GSE202128 |
Single cell RNA-seq in chicken's lung and DF-1 cell line |
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| Relations |
| BioSample |
SAMN28058350 |
| SRA |
SRX15132979 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6098325_Herts33minus.tar.gz |
52.8 Mb |
(ftp)(http) |
TAR |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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