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Impact of azide metabolic glycoengineering and bioorthogonal click chemistry cell-cell tethering in adipose tissue-derived mesenchymal stromal cells

GSE231507 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2024/04/15 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 15 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
Homo sapiens (human)
Samples
15 samples: 5 × 2D_Native vs 5 × 2D_Glycoengineered vs 5 × 3D_Day 1Groups 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)
We report RNA sequencing data from adipose tissue-derived mesenchymal stromal cells (hASCs) functionalized via metabolic glycoengineering with pendant azide sialosides. Beyond the impact of this cell surface engineering on transcriptome, we explore the expression profiles of azide-hASCs covalently tethered to an interfacial glycosaminoglycan via bioorthogonal click chemistry and yielding a 3D multicellular construct.
Read the full summary ↓
We find global gene expression changes in response to metabolic glycoengineering in 2D cell culture conditions as well as those arising from cell-to-cell tethering into 3D environments.
This dataset
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.

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 GSE231507_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA949992 and SRA study SRP429954. Searching any of these in the dataset finder brings you back here.

Study design
5 × 2D_Native vs 5 × 2D_Glycoengineered vs 5 × 3D_Day 1

Supports a between-group comparison across 15 samples.

How we read this: 3 replicated groups read from 15 sample titles; they account for 15 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
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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.