NextSeq 500 paired end sequencing; GSM2363918: A1-0h; Mus musculus; RNA-Seq

A1-0h

We have developed a computational approach that uses self-organizing maps for integrative genomic analysis. We utilize this approach to identify the single-cell chromatin and transcriptomic profiles during mouse pre-B cell differentiation. Overall design: We use the C1 Fluidigm system to profile gene expression and chromatin accessibility in single-cells during pre-B cell differentiation.

Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps [scRNA-Seq]

Submitted by Gene Expression Omnibus on 16-FEB-2019

Single cells were isolated using the Fluidigm C1 System. Single cell C1 runs were completed using the smallest IFC (5-10 um) based on the estimated size of B3 cells. Briefly, cells were collected for 0,2,6,12,18 and 24 hour time-points at a concentration of 400 cells/μl in a total of 50 μl. To optimize cell capture rates on the C1, buoyancy estimates were optimized prior to each run. Each individual C1 capture site was visually inspected to ensure single-cell capture and cell viability. After visualization, the IFC was loaded with Clontech SMARTer kit lysis, RT, and PCR amplification reagents. cDNA was normalized across all libraries from 0.1-0.3 ng/μl and libraries were constructed using Illumina’s Nextera XT library prep kit per Fluidigm’s protocol. Constructed libraries were multiplexed and purified using AMPure beads. The final multiplexed single-cell library was analyzed on an Agilent 2100 Bioanalyzer for fragment distribution and quantified using Kapa Biosystem’s universal library quantification kit. The library was normalized to 2 nM and sequenced as 75bp paired-end dual indexed reads using Illumina’s NextSeq 500 system at a depth of ~1.0-2.0 million reads per library.

Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps [scRNA-Seq]

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