2021
DOI: 10.1016/j.xpro.2021.100989
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Preparation of mouse pancreatic tumor for single-cell RNA sequencing and analysis of the data

Abstract: Summary Preparation of single-cell suspension from primary tumor tissue can provide a valuable resource for functional, genetic, proteomic, and tumor microenvironment studies. Here, we describe an effective protocol for mouse pancreatic tumor dissociation with further processing of tumor suspension for single-cell RNA sequencing analysis of cellular populations. We further provide an outline of the bioinformatics processing of the data and clustering of heterogeneous cellular populations comprising … Show more

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Cited by 5 publications
(4 citation statements)
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“…The analysis of sc sequencing data includes several stages and can be roughly divided into preprocessing that assigns reads to cells and genes or peaks, low-quality cell removal, dimensionality reduction, clustering, and cell annotation steps [32, 33]. Each of these can be accomplished by running one or several pipelines designed for a specific sequencing data type (scRNA-Seq, scATAC-Seq or scMultiome).…”
Section: Methodsmentioning
confidence: 99%
“…The analysis of sc sequencing data includes several stages and can be roughly divided into preprocessing that assigns reads to cells and genes or peaks, low-quality cell removal, dimensionality reduction, clustering, and cell annotation steps [32, 33]. Each of these can be accomplished by running one or several pipelines designed for a specific sequencing data type (scRNA-Seq, scATAC-Seq or scMultiome).…”
Section: Methodsmentioning
confidence: 99%
“…Afterwards, red blood cells were lysed with ACK Lysing buffer for 5 min, washed again in staining buffer and cells proceeded to staining for flow cytometry analysis. For single cell RNA sequencing analysis, the suspensions were subjected to an extra step for dead cell removal using (Dead Cell Removal Kit, Miltenyi Biotec) (207).…”
Section: Single Cell Suspensions From Pancreatic Tumorsmentioning
confidence: 99%
“…Preprocessing Bioinformatics analysis of single-cell RNA sequencing data was conducted as described previsoly 76 Low quality cell removal Low quality cells were removed in two iterations. First, the following filtering thresholds were applied per cell -from 80 to 6000 genes, minimum 500 uniquely mapped fragments, maximum 5 percent of transcripts mapped to mitochondrial genes.…”
Section: Single Cell Rna-sequencing (Scrna-seq) Bioinformatic Pipelinementioning
confidence: 99%