GEO series

Lipid nanoparticles as a tool to dissect dendritic cell maturation pathways

GSE279232 Mus musculus (mouse) Single-cell RNA-seq 27 samples Submitted 2025/07/01 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 27 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: 27 samples: 27 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.
Paper: Lipid nanoparticles as a tool to dissect dendritic cell maturation pathwaysCell reports · 2025

Summary (written by the submitters)

Dendritic cells (DCs) are short-lived immune cells that continuously roam our body in search for foreign or self-antigens. Upon acquisition of antigen, they mature and start migrating to the lymph node to present the antigen to naïve T cells. Depending on the context wherein the antigen is acquired, DCs will mature in a homeostatic or immunogenic manner.

Read the full summary ↓

So far, the field is lacking proper tools to distinguish between the two maturation states. Most maturation markers are shared between the two states and therefore inappropriate to use. Still, defining the proper maturation type is crucial as it determines how the DCs will instruct the T cells towards antigen expressing cells. In this study, we used a lipid nanoparticle (LNP)-based approach to steer DC maturation pathways in vivo. CITE-seq analysis allowed us to design a panel of flow cytometry markers that reliably annotates the two DC maturation states, as validated in an infection and in a tumor model. Furthermore, the data corroborated that uptake of empty LNPs in DCs induces their homeostatic maturation, in contrast to uptake of mRNA-LNPs or TLR ligand-adjuvanted LNPs, leading to distinct effector T cell outputs. This reveals that LNPs themselves are not being decoded as “danger” by cDC1s, and that the cargo is essential to provide adjuvants activity, which is highly relevant for targeted design of LNP-based therapies.

Published in

Lipid nanoparticles as a tool to dissect dendritic cell maturation pathways

Rennen S, Bosteels V, De Nolf C et al. · Cell reports 2025 · PMID 40828654 · doi:10.1016/j.celrep.2025.116150

This dataset

Open GSE279232 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 GSE279232_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 PRJNA1171441 and SRA study SRP539516. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40828654) ↗

Study design

27 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 27 sample titles: 27 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

GSM8564980 Spleen, cDC1s, Steady State, 0h, ADT-derived cDNA
GSM8564981 Spleen, cDC1s, Steady State, 0h, mRNA-derived cDNA
GSM8564982 Spleen, cDC1s, Steady State, 0h, HTO-derived cDNA
GSM8564983 Spleen, cDC1s, eLNP, 8h, ADT-derived cDNA
GSM8564984 Spleen, cDC1s, eLNP, 8h, mRNA-derived cDNA
GSM8564985 Spleen, cDC1s, eLNP, 8h, HTO-derived cDNA
GSM8564986 Spleen, cDC1s, pIC-LNP, 8h, ADT-derived cDNA
GSM8564987 Spleen, cDC1s, pIC-LNP, 8h, mRNA-derived cDNA
GSM8564988 Spleen, cDC1s, pIC-LNP, 8h, HTO-derived cDNA
GSM8564989 Spleen, cDC1s, CpG-LNP, 8h, ADT-derived cDNA
GSM8564990 Spleen, cDC1s, CpG-LNP, 8h, mRNA-derived cDNA
GSM8564991 Spleen, cDC1s, CpG-LNP, 8h, HTO-derived cDNA
GSM8564992 Spleen, cDC1s, pIC alone, 8h, ADT-derived cDNA
GSM8564993 Spleen, cDC1s, pIC alone, 8h, mRNA-derived cDNA
GSM8564994 Spleen, cDC1s, pIC alone, 8h, HTO-derived cDNA
GSM8564995 Spleen, cDC1s, eLNP, 2h, ADT-derived cDNA
GSM8564996 Spleen, cDC1s, eLNP, 2h, mRNA-derived cDNA
GSM8564997 Spleen, cDC1s, eLNP, 2h, HTO-derived cDNA
GSM8564998 Spleen, cDC1s, pIC-LNP, 2h, ADT-derived cDNA
GSM8564999 Spleen, cDC1s, pIC-LNP, 2h, mRNA-derived cDNA
GSM8565000 Spleen, cDC1s, pIC-LNP, 2h, HTO-derived cDNA
GSM8565001 Spleen, cDC1s, CpG-LNP, 2h, ADT-derived cDNA
GSM8565002 Spleen, cDC1s, CpG-LNP, 2h, mRNA-derived cDNA
GSM8565003 Spleen, cDC1s, CpG-LNP, 2h, HTO-derived cDNA
GSM8565004 Spleen, cDC1s, pIC alone, 2h, ADT-derived cDNA
GSM8565005 Spleen, cDC1s, pIC alone, 2h, mRNA-derived cDNA
GSM8565006 Spleen, cDC1s, pIC alone, 2h, HTO-derived cDNA

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