GEO series

Integrin/CD44-Targeted Liposomal Co-Delivery of Nintedanib and Dexamethasone for Acute Exacerbation of Idiopathic Pulmonary Fibrosis

GSE303579 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/09/27 Platform GPL28457

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 4 × lung, con vs 4 × lung, AE vs 4 × lung, RHLGroups 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)

Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) poses a significant clinical challenge due to its high morbidity and mortality, coupled with a lack of effective targeted therapies. Here, utilizing single-cell transcriptomic analysis, we identified integrin and CD44 as markedly upregulated in injured alveolar type II cells and myofibroblasts, highlighting their potential as pathological delivery targets in AE-IPF.

Read the full summary ↓

Based on these findings, we developed a dual-targeted liposomal nanoplatform (ND-RHL) co-encapsulating nintedanib and dexamethasone, specifically engineered to exploit integrin/CD44 overexpression for pulmonary precise drug delivery and synergistic anti-fibrotic and anti-inflammatory effects. ND-RHL exhibited favorable physicochemical characteristics, efficient dual-drug loading, and selective accumulation in integrin/CD44-high cells both in vitro and in vivo. In a murine model of AE-PF, intratracheal administration of ND-RHL markedly improved survival, mitigated lung inflammation and fibrosis, and preserved pulmonary architecture, with minimal systemic toxicity. Mechanistically, transcriptomic profiling and immune phenotyping demonstrated that ND-RHL reversed AE-induced gene expression patterns and inhibited pivotal signaling pathways, including PI3K–AKT–mTOR, Wnt/β-catenin, and NF-κB–PPARγ, thereby orchestrating the remodeling of both immune and extracellular matrix microenvironments. This study presents ND-RHL as a mechanistically informed, cell-targeted nanotherapeutic with robust therapeutic potential and translational promise for the treatment of AE-IPF and related fibrotic lung diseases.

This dataset

Open GSE303579 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 GSE303579_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 PRJNA1295858 and SRA study SRP603243. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × lung, con vs 4 × lung, AE vs 4 × lung, RHL

Supports a between-group comparison across 12 samples.

How we read this: 3 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

GSM9130972 lung, con1 lung, con
GSM9130973 lung, con2 lung, con
GSM9130974 lung, con3 lung, con
GSM9130975 lung, con4 lung, con
GSM9130976 lung, AE1 lung, AE
GSM9130977 lung, AE2 lung, AE
GSM9130978 lung, AE3 lung, AE
GSM9130979 lung, AE4 lung, AE
GSM9130980 lung, RHL1 lung, RHL
GSM9130981 lung, RHL2 lung, RHL
GSM9130982 lung, RHL3 lung, RHL
GSM9130983 lung, RHL4 lung, RHL

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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.