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Sample GSM6510411

Query DataSets for GSM6510411

Status

Public on Nov 23, 2022

Title

Input library sequencing; RNA-seq

Sample type

SRA

Source name

Synthetic plasmid library

Organism

synthetic construct

Characteristics

tissue: Synthetic plasmid library

Treatment protocol

Input STARR-seq plasmid libraries were delivered to developing mouse cerebral cortices (E14.5/E15.5) via in utero electroporation and harvested 18 hours post electroporation.

Extracted molecule

genomic DNA

Extraction protocol

Electroporated brains were harvested and dissected in ice-cold sterile PBS. Meninges were removed and GFP+ portions of the cortices were incubated at 37◦C for 10 minutes in 0.25% trypsin-EDTA supplemented with 0.1% DNAse I. Following incubation, the trypsin solution was removed and replaced with ice- cold 10% FBS/HBSS/Propidium iodide supplemented with 0.01% DNAse I. A single cell suspension was then generated by trituration with a fire-polished glass pipette and filtered with a 30 μm cell strainer. Cells were then stained with the LIVE/DEAD Near-IR Dead Cell Stain per manufacturer’s instructions. Following staining, viable GFP+ cells were bulk sorted using a FACS Aria II cytometer.
For input normalization: input plasmid libraries were fragmented and sequenced with the NEBNext Ultra II FS DNA Library Prep Kit per manufacturer's instrutions.
For scRNA-seq: library was performed according to the manufacter’s instructions (Chromium Next GEM Single Cell 3’ Reagent Kit v3.1 protocol, 10x Genomics). Briefly, GCs were resuspended in the master mix and loaded together with partitioning oil and gel beads into the chip to generate the gel bead-in-emulsion (GEM). The poly-A RNA from the cell lysate contained in every single GEM was retrotranscripted to cDNA, which contains an Ilumina R1 primer sequence, Unique Molecular Identifier (UMI) and the 10x Barcode. The pooled barcoded cDNA was then cleaned up with Silane DynaBeads, amplified by PCR and the apropiated sized fragments were selected with SPRIselect reagent for subsequent library construction. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.
scRNA-seq/WGS

Library strategy

OTHER

Library source

genomic

Library selection

other

Instrument model

Illumina MiSeq

Description

FS DNA Library Prep Kit

Data processing

The demultiplexing, barcoded processing, gene counting and aggregation were made using the Cell Ranger software v6.0 (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger)
Cluster analysis was performed using Seurat v4.0 (https://satijalab.org/seurat/). For each library, cells were removed which contained 200 or fewer genes or more than 5,000 genes. Each library was independently normalized and 2,000 highly variable features were identified for each library. Cells across independent libraries at were integrated for joint analysis via canonical correlation analysis. Variation in gene expression based on cell-cycle related genes was regressed from cluster analysis in dataset scaling using an annotated set of G2M and S phase related genes provided in Seurat. k-nearest neighbors (k=20) were calculated in the space of significant principal components (in this case, 30 principal components) and clustering was performed with the Louvain-Jaccard method. Cell type cluster and metacluster identities were assigned on the basis of marker gene expression.
For input normalization, fastq reads from the input library were aligned using the Burrows-Wheeler Aligner (https://github.com/lh3/bwa) to a custom reference genome including the mouse assembly mm10 with additional fasta records containing the sequences of each STARR-seq library construct (constructList.txt)
To measure enhancer activity in pseudo-bulk tissue, we we implemented gonomics: fastqFilter - collapseUmi (available from https://github.com/vertgenlab/gonomics) to remove unique molecular identifier (UMI) duplicates from our 10x libraries. We then used gonomics: fastqFormat -singleCell to parse the cell barcode and UMI from R1 into the read name for the R2 fastq. We then used the BWA to align reads to our custom STARR-seq reference genome described above. The enhancer activity score for each construct was then calculated as the input- normalized UMI count per 1000 total reporter UMI counts.
To perform cell-type specific enhancer activity quantification (as seen in pooledClusterCounts.txt), reads from each library aligned to the custom STARR-seq reference genome described above were sorted by cell barcode using gonomics: mergeSort -singleCellBx and input-normalized count matrices were generated with gonomics: scCount. Input-normalized count matrices were then partitioned by metacluster using cluster identities determined for each cell barcode by Seurat. Cells with fewer than 4 pCAG-GFP UMIs were discarded. The input-normalized reporter UMI counts for each cell were then further normalized to the pCAG-GFP UMI count for that cell. The cell type enhancer activity score was then calculated as the average transfection-normalized, input-normalized UMI count per cell in each cluster.
Assembly: mm10
Supplementary files format and content: Tab-separated values files and matrix files

Submission date

Aug 26, 2022

Last update date

Nov 23, 2022

Contact name

Craig Barrett Lowe

Organization name

Duke University School of Medicine

Department

Molecular Genetics and Microbiology

Street address

213 Research Dr