GEO series

Specific pre-plasma cell states and local proliferation at the dark zone – medulla interface characterize germinal center-derived plasma cell differentiation in lymph node (Figure2) III

GSE275123 Mus musculus (mouse) Single-cell RNA-seq 21 samples Submitted 2024/08/30 Platform GPL30172

At a glance

Single-cell RNA-seq · mouse · 21 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 21 samples: 21 samples, each with a distinct title — no replicated groupsGroups 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)

High affinity antibody-producing plasma cell (PC) production in germinal centers (GC) is crucial for antibody-mediated immune protection after vaccination or infection. The selection of high affinity B cells in the GC light zone instructs PC differentiation in a subset of cells, but the phenotype, differentiation trajectory and spatial localization of those prePC intermediates remain to be characterized.

Read the full summary ↓

Here, we have used a mouse model to track GC-derived B cells with integrative single-cell and spatial analyses in draining lymph node after immunization. We first identified putative prePC in scRNA-seq datasets, then enriched those cells through their specific surface phenotype for further analysis of their gene expression trajectories and BCR repertoire. We found a continuum of actively proliferating transitional states bridging selected LZ GC B cells and recently exported PCs, with gradually increasing levels of endoplasmic reticulum stress-associated genes and immunoglobulin transcripts. Spatial analyses revealed that recently differentiated PC continued their maturation and proliferation at the interface between the DZ and extensions of the lymph node medulla. Our results provide insights into the intermediate stages and microenvironmental factors involved in the differentiation of GC B cells into PC, with implications for vaccine development and understanding antibody responses.

This dataset

Open GSE275123 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 GSE275123_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1149486 and SRA study SRP527214. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

21 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 21 sample titles: 21 distinct titles with little repetition. 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

GSM8465939 [BCR_NonPC_S12]
GSM8465940 [BCR_PC_S11]
GSM8465941 [BCR_PC_S9]
GSM8465942 [GEX_NonPC_1_S5]
GSM8465943 [GEX_NonPC_1_S6]
GSM8465944 [GEX_NonPC_3_S7]
GSM8465945 [GEX_NonPC_4_S8]
GSM8465946 [GEX_NonPC_5_S5]
GSM8465947 [GEX_NonPC_6_S6]
GSM8465948 [GEX_NonPC_7_S7]
GSM8465949 [GEX_NonPC_8_S8]
GSM8465950 [GEX_PC_1_S1]
GSM8465951 [GEX_PC_2_S2]
GSM8465952 [GEX_PC_3_S3]
GSM8465953 [GEX_PC_4_S4]
GSM8465954 [GEX_PC_5_S1]
GSM8465955 [GEX_PC_6_S2]
GSM8465956 [GEX_PC_7_S3]
GSM8465957 [GEX_PC_8_S4]
GSM8465958 [HTO_NonPC_S10]
GSM8465959 [HTO_PC_S9]

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