← BioTransfer GEO Dataset Finder
GEO series

Siglec-1−/FSClow alveolar macrophages exhibit pro-inflammatory phenotypes in chronic obstructive pulmonary disease

GSE199345 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/03/24 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
24 samples: 24 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)
Alveolar macrophages (AMs) in patients with chronic obstructive pulmonary disease (COPD) orchestrate persistent inflammation in the airway, however, sub-populations of alveolar macrophages predominantly participating in chronic inflammation have been poorly characterised. We recently reported that Siglec-1 was involved in recognition and engulfment of non-typeable Haemophilus influenzae and that the expression of Siglec-1 on AMs was decreased in COPD lung tissues.
Read the full summary ↓
Here, based on the expression of Siglec-1 and the intensity of forward scattergram (FSC) using flow cytometry, we identified three subsets present in alveolar macrophages: Siglec-1+/FSChi, Siglec-1−/FSChi, Siglec-1−/FSClow subsets. The Siglec-1−/FSClow subset increased in number in the lungs of COPD subjects. RNA-sequencing revealed that the Siglec-1−/FSClow subset exhibited pro-inflammatory and protease-producing phenotypes compared to other two subsets. Clinically, a decrease in the intensity of Siglec-1 expression in macrophages of induced sputum from COPD subjects was correlated with exacerbation rates in prospective four years. Collectively Siglec-1− AMs showed the pro-inflammatory properties and that their emergence in COPD airways was a risk factor in exacerbation. The Siglec-1−/FSClow subset might serve as “pathogenic macrophages” which could lead to poor clinical outcomes for patients with COPD
This dataset
Open GSE199345 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 GSE199345_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA819427 and SRA study SRP365723. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.