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Sample GSM6083547

Query DataSets for GSM6083547

Status

Public on May 01, 2024

Title

Sample type

SRA

Source name

tomato leaf

Organism

Solanum lycopersicum

Characteristics

tissue: leaf
age: 30 days
treatment: ToCV infection

Extracted molecule

total RNA

Extraction protocol

The leaves of tomato plants were cut and immediately transferred into the enzyme solutionnby vacuum infiltration for 30 min. The samples were then protoplasted for 2 hours at 20℃ on an orbital shaker set at 200 g. Cells were then filtered with a 40 µm cell strainer.The WI solution was added and shaken vigorously to release the protoplasts. The protoplasts were collected by centrifugation at 200 g, and then washed with the WI solution after which the supernatant was removed by centrifugation. Cell activity was detected by trypan blue staining and cell concentration was measured with a hemocytometer.
The scRNA-seq libraries were constructed using the Chromium Single Cell 3’ Gel Beads-in-emulsion (GEM) Library & Gel Bead Kit v3.

Library strategy

RNA-Seq

Library source

transcriptomic single cell

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Data processing

The Cell Ranger software pipeline (version 3.1.0) provided by 10×Genomics was used to demultiplex cellular barcodes, map reads to the genome and transcriptome using the STAR aligner, and down-sample reads as required to generate normalized aggregate data across samples, producing a matrix of gene counts versus cells. We processed the unique molecular identifier (UMI) count matrix using the R package Seurat (version 3.1.1).
To remove low quality cells and likely multiplet captures, which is a major concern in microdroplet-based experiments, we applied a criteria to filter out cells with UMI/gene numbers out of the limit of mean value +/- 2 fold of standard deviations assuming a Guassian distribution of each cells' UMI/gene numbers. Following visual inspection of the distribution of cells by the fraction of mitochondrial genes expressed, we further discarded low-quality cells where >10% of the counts belonged to mitochondrial genes.
Library size normalization was performed with NormalizeData function in Seurat to obtain the normalized count. Specifically, the global-scaling normalization method “LogNormalize” normalized the gene expression measurements for each cell by the total expression, multiplied by a scaling factor (10,000 by default), and the results were logtransformed.
The most variable genes were selected using FindVariableGenes function(mean.function = ExpMean, dispersion.function = LogVMR) in Seurat.Principal component analysis (PCA) was performed to reduce the dimensionality with RunPCA function in Seurat. 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 2-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
Differentially expressed genes(DEGs) were identified using the FindMarkers function(test.use = MAST) in Seurat. P value < 0.05 and |log2foldchange| > 0.58 was set as the threshold for significantly differential expression. GO enrichment and KEGG pathway enrichment analysis of DEGs were respectively performed using R based on the hypergeometric distribution.
Assembly: Solanum_lycopersicum_v4.0
Supplementary files format and content: Matrix table with raw gene counts for every gene and every sample

Submission date

Apr 30, 2022

Last update date

May 01, 2024

Contact name

Hao Yue

Organization name

Hunan university

Street address

No. 892 Yuanda 2nd Road, Mapoling, Furong District, Changsha City, Hunan Province

City

Changsha