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Integrative analysis reveals ciliary dysfunction and compartmentalized LCN2 expression as distinct features of COVID-19

GSE318035 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2026/02/03 Platform GPL29480
At a glance

Single-cell RNA-seq · human · 15 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
Homo sapiens (human)
Samples
15 samples: 10 × BALF from Influenza patient vs 5 × BALF from COVID-19 patientGroups 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)
The unique pulmonary pathophysiology of COVID-19 compared with other respiratory viral infections remains poorly understood. We performed integrative bulk and single-cell RNA sequencing on bronchoalveolar lavage fluid (BALF) from patients with COVID-19 (n=24) or influenza (n=10) and healthy controls (n=10). While influenza primarily induced conventional inflammatory responses, COVID-19 triggered distinct microenvironmental remodeling characterized by a specific cilium-related gene signature (e.g., DNAH9, FOXJ1) and expansion of ciliated and airway epithelial cells.
Read the full summary ↓
Cross-tissue comparison with peripheral blood mononuclear cells uncovered a compartmentalized immune response, particularly for Lipocalin-2 (LCN2), which showed opposite expression trends between the lung and systemic circulation. This study identifies ciliary dysfunction and epithelial-immune crosstalk as key features distinguishing COVID-19 from influenza and highlights epithelial-derived LCN2 as a robust, compartmentalized biomarker for disease severity.
This dataset
Open GSE318035 in finder → Find more human 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 GSE318035_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 PRJNA1416361 and SRA study SRP670477. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 × BALF from Influenza patient vs 5 × BALF from COVID-19 patient

Supports a between-group comparison across 15 samples.

How we read this: 2 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.