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System and detection method for identifying cell types of arabidopsis thaliana leaves

A technology for Arabidopsis cotyledon and single-cell sequencing, which is applied in biochemical equipment and methods, microbial determination/inspection, biostatistics, etc. Power consumption etc.

Pending Publication Date: 2021-04-30
HENAN UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] For Arabidopsis cotyledon samples, there is currently no directly available reference data set to automatically and quickly match and identify cell types. Manual identification of marker genes alone is time-consuming and labor-intensive, with a low degree of automation and low accuracy for identification of similar cell types.

Method used

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  • System and detection method for identifying cell types of arabidopsis thaliana leaves
  • System and detection method for identifying cell types of arabidopsis thaliana leaves
  • System and detection method for identifying cell types of arabidopsis thaliana leaves

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0097] Embodiment 1, manual identification

[0098] Firstly, the Marker genes were collected by consulting a large amount of literature, and the gene expression clustering heat map and the expression level map (FeaturePlot) in a single cell were drawn, so as to manually identify the cell types representing different stages of stomatal development in Arabidopsis cotyledons, specifically The Marker gene used is as follows:

[0099] Mesophyll cells (MPC): RBCS, LHCB

[0100] Meristemoid cells (MMC): HDG2, POLAR, SPCH, TMM, MUTE, EPF2

[0101] Early meristem cells (EM): MUTE, BASL, SPCH, EPF2

[0102] Late meristem cells (LM): BASL, MUTE, EPF1

[0103] Guard mother cells (GMC): EPF1, HIC, FAMA, SCRM

[0104] Young guard cells (YGC): RBCS, FAMA, EPF1

[0105] Guard cells (GC): low expression of RBCS, FAMA, SCRM, and TMM genes

[0106] Squamous cells (PC): IQD5, RBCS

[0107] To plot the expression level of a gene in a single cell, use the following code:

[0108]

Embodiment 2

[0109] Example 2, identification method based on singleR reference data set

[0110] Based on the Arabidopsis cotyledon cell types identified above, a reference data set for each cell type was constructed according to its expression profile, which is used for rapid judgment of Arabidopsis cotyledon cell types in high-throughput single-cell transcriptome sequencing. Specific operations Proceed as follows:

[0111] Step 1. Import the data to be tested;

[0112] seurat_ob = readRDS("seurat_ob.rds")

[0113] query.m=GetAssayData(seurat_ob, assay="RNA", slot="counts")

[0114] query.sce=SingleCellExperiment(assays=list(counts=query.m))

[0115] query.sce = logNormCounts(query.sce)

[0116] Step 2. Load the constructed Arabidopsis reference data set;

[0117] ref.sce = readRDS("reference.rds")

[0118] Step 3, use the SingleR () function to identify the cell type;

[0119] pred=SingleR(query.sce,ref.sce,labels=factor(ref.sce$celltype),BPPARAM=

[0120] MulticoreParam(workers...

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Abstract

The invention relates to a system and a detection method for identifying cell types of Arabidopsis thaliana leaves based on single-cell sequencing, and compared with manual identification, the cell types representing different stages of stomatal development in the Arabidopsis thaliana leaves can be rapidly obtained only by inputting to-be-identified data. About 10,000 cells can be identified within about 10 minutes, so that the labor cost is greatly reduced, and the annotation precision is ensured.

Description

technical field [0001] The invention belongs to the technical field of transcriptome sequencing, and specifically relates to a system and a detection method for identifying Arabidopsis cotyledon cell types based on single-cell transcriptome sequencing data. Background technique [0002] In the field of high-throughput single-cell transcriptome sequencing analysis, cell type identification is a crucial link. Cell type identification analysis can effectively reveal the heterogeneity of complex cell populations and construct cell maps. At present, there are two methods for cell type identification, one is marker-based identification based on specific Marker genes, and the other is identification based on single-cell reference data sets. Using the former marker gene (marker-based) manual identification method means that researchers must consult a large amount of literature to collect markers, which is time-consuming and labor-intensive, and many cell types cannot be well disting...

Claims

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Application Information

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IPC IPC(8): G16B30/00G16B40/00G16B45/00C12Q1/6881
CPCG16B30/00G16B40/00G16B45/00C12Q1/6881C12Q2600/158
Inventor 孙旭武肖云平刘祉辛殷昊陆瑶巴永兵
Owner HENAN UNIVERSITY