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Genomics controller

Manufactured by 10x Genomics

The 10x Genomics Controller is a core hardware component designed to operate and control the company's proprietary microfluidic systems. It serves as the central processing unit, managing the precise fluidic operations and environmental conditions required for the execution of 10x Genomics' genomic analysis workflows.

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2 protocols using genomics controller

1

Single-cell RNA-seq of Tumor-Infiltrating Cells

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Tumors were harvested on day 17 and viable CD45+ cells were FACS sorted. Single-cell emulsions were obtained using the 10x Genomics Controller and the v2 Library and Gel Bead kit (10X Genomics). RNA-sequencing libraries were prepared as instructed by the 10× 3’ v2 kit protocol. Resulting libraries were sequenced on an Illumina NextSeq using a NextSeq 500/550 v2.5 High Output Kit.
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2

Single-cell RNA-seq of HFHC diet mouse cells

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Approximately 10,000 viable enriched stromal cells were loaded on a 10x Genomics controller (10x Genomics, San Francisco, CA) to generate barcoded single-cell Gel Bead in-Emulsions (GEMs) using the 10x Genomics 3′ kit as previously described.31 (link) Complementary DNA libraries were sequenced using a NovaSeq 6000 (Illumina, San Diego, CA) at the University of Colorado Genomics Shared Resource Core. To control for batch effects 3 independent sequencing experiments were performed which included both control- and HFHC diet–fed mice. Quantification and analysis of single cell sequencing was performed using the R packages Seurat and scran as previously described.31 (link) Ridge plots were generated using Seurat’s RidgePlot function. Cells were classified and correlation plots were generated using clustifyr.55 (link) For cell cycle analysis, Seurat assigns each cell a score based on its expression of G2/M and S phase markers. These marker sets are anti-correlated in their expression levels, and cells expressing neither are assigned to G1 phase. Cells with fewer than 250 detectable genes or >20% of unique molecular identifiers (UMIs) derived from mitochondrial genes were excluded from the analysis to eliminate cells with insufficient expression data for clustering and dead cells, respectively.
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