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Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma – Visium_FFPE_Spatial_Trasciptomics_project

GSE260998 Mus musculus (mouse) Spatial transcriptomics 2 samples Submitted 2024/04/17 Platform GPL21626
At a glance

Spatial transcriptomics · mouse · 2 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Mus musculus (mouse)
Samples
2 samples
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.
Paper
Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphomaResearch square · 2024
Summary (written by the submitters)
The germinal center (GC) dark zone (DZ) and light zone (LZ) regions spatially separate expansion and diversification from selection of antigen-specific B-cells to ensure antibody affinity maturation and B cell memory. How GC polarity is established is poorly understood. This study provides novel insights into signals controlling asymmetric T-cell distribution between DZ and LZ regions.
Read the full summary ↓
We identify spatially-resolved DNA damage response and chromatin compaction molecular features precluding DZ T-cell infiltration. The DZ spatial transcriptional signature linked to T-cell immune evasion clustered aggressive Diffuse Large B-cell Lymphomas (DLBCL) for differential T cell infiltration. We reveal the dependence of the DZ transcriptional core signature on the ATR kinase and dissect its role in restraining inflammatory responses contributing to estabslish an immune-repulsive imprint in DLBCL. These insights may guide ATR-focused treatment strategies bolstering immunotherapy in tumors marked by DZ transcriptional and chromatin-associated features.
Published in
Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma
Cancila V, Morello G, Bertolazzi G et al. · Research square 2024 · PMID 38562878 · doi:10.21203/rs.3.rs-4093618/v1
This dataset
Open GSE260998 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 GSE260998_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1084805 and SRA study SRP493674. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38562878) ↗
Samples in this study

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