GEO series

Next Generation Sequencing Facilitates Quantitative Analysis of WT and GSH or Ctrl and Cas9 mouse breast cancer cells Transcriptomes

GSE252087 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/10/09 Platform GPL21103

At a glance

RNA-seq (gene expression) · mouse · 12 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 12 samples: 3 × WT vs 3 × GSH vs 3 × Ctrl vs 3 × Cas9Groups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to analysis the differiational genes and pathways in WT and WT+GSH or Ctrl-Cas9 and Scarb2-Cas9 mouse breast cancer cells by using NGS-derived transcriptome profiling (RNA-seq). Methods: 4T1 cells treated with or without GSH (500 µM) and 4T1 stably expressing Ctrl-Cas9 or Scarb2-Cas9 were collected.

Read the full summary ↓

These cells' mRNA profiles were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with following methods: Alignment by using HISAT2 v2.1, IGV was used to to view the mapping result by the Heatmap, histogram, scatter plot or other stytle, FPKM was then calculated to estimate the expression level of genes in each sample, DEGseq v1.18.0 was used for differential gene expression analysis between two samples with non biological replicates and Function Enrichment Analysis including GO enrichment analysis and KEGG . Conclusions: Our study represents the first detailed analysis of WT and WT+GSH or Ctrl-Cas9 and Scarb2-Cas9 mouse breast cancer cells' transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

This dataset

Open GSE252087 in finder → Find more mouse RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE252087_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1057739 and SRA study SRP480234. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × WT vs 3 × GSH vs 3 × Ctrl vs 3 × Cas9

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM7993499 WT-1 WT
GSM7993500 WT-2 WT
GSM7993501 WT-3 WT
GSM7993502 GSH-1 GSH
GSM7993503 GSH-2 GSH
GSM7993504 GSH-3 GSH
GSM7993505 Ctrl-1 Ctrl
GSM7993506 Ctrl-2 Ctrl
GSM7993507 Ctrl-3 Ctrl
GSM7993508 Cas9-1 Cas9
GSM7993509 Cas9-2 Cas9
GSM7993510 Cas9-3 Cas9

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.