GEO series

Epigenetic and Post-transcriptional Regulation of LYPD2 Modulates Smoking-associated Phenotype via CHRNA7 and DNMT1 in the Human Airway Epithelium

GSE135624 Homo sapiens (human) Single-cell RNA-seq 66 samples Submitted 2025/06/05 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 66 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: 66 samples: 66 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)

Background: The small airway epithelium (SAE, >6th generations), the initiation site of smoking-induced airway disorders, is highly sensitive to the stress of smoking. Lymphocyte antigen-6 (Ly6)/urokinase-type plasminogen activator receptor (uPAR, also called PLAUR) superfamily of proteins function in differentiation and inflammation, common phenotypes altered in smoking-related lung diseases including chronic obstructive pulmonary disease (COPD) and lung cancer.

Read the full summary ↓

In the human small airway epithelial (SAE) transcriptome, LYPD2 (LY6/PLAUR domain containing 2), a gene with unknown function, is the most down-regulated Ly6/uPAR gene in smokers. Based on the knowledge that SLURP1 (secreted LY6/PLAUR domain containing 1), a paralog of LYPD2, modulates inflammatory phenotypes, we hypothesized that LYPD2 plays a role in the smoking-related phenotypes in the airway epithelium. RNA sequencing, single cell RNA sequencing and immunohistochemistry demonstrated that LYPD is expressed in airway epithelial cells, predominantly in club and intermediate cells. Quantification of LYPD2 expression in the airway epithelium demonstrated a significant suppression of LYPD2 in smokers, overall in the SAE, and specifically in club, mucous and intermedaite cells. Cigarette smoking extract (CSE) treatment of differentiating airway basal stem/progenitor cells (BC) in air liquid interface culture (ALI) demonstrated significantly down-regulated LYPD2 expression. LYPD2 expression correlated with the expression of tight junction and differentiation-related marker genes in vivo and LYPD2 down-regulation in airway epithelium in vitro resulted in an increased expression of smoking-related inflammatory genes accompanied by decreased airway transepithelial resistance, suppressed junctional gene expression, and diminished gene expression related to normal differentiation. We show the function of LYPD2 is likely mediated by CHRNA7 and DNMT1, associated with JNK and NF-κB signaling and can be modulated by miR-214-3p and epigenetic mechanisms.

This dataset

Open GSE135624 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 GSE135624_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 PRJNA564730 and SRA study SRP221209. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

66 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 66 sample titles: 66 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

GSM4023655 DGM-10123_M12_sm_RNA-Seq
GSM4023656 DGM-10254_M12_sm_RNA-Seq
GSM4023657 DGM-10161_M12_sm_RNA-Seq
GSM4023658 DGM-10288_M12_sm_RNA-Seq
GSM4023659 DGM-10383_M12_sm_RNA-Seq
GSM4023660 DGM-10380_M12_sm_RNA-Seq
GSM4023661 DGM-10343_M12_sm_RNA-Seq
GSM4023662 DGM-10270_M12_sm_RNA-Seq
GSM4023663 DGM-10194_M12_sm_RNA-Seq
GSM4023664 DGM-10565_M12_sm_RNA-Seq
GSM4023665 DGM-10500_M6_sm_RNA-Seq
GSM4023666 DGM-10503_M12_sm_RNA-Seq
GSM4023667 DGM-10475_M3_sm_RNA-Seq
GSM4023668 DGM-01774_M3_sm_RNA-Seq
GSM4023669 DGM-00342_M12_sm_RNA-Seq
GSM4023670 DGM-10261_M0_sm_RNA-Seq
GSM4023671 DGM-10568_M0_sm_RNA-Seq
GSM4023672 DGM-10707_M0_sm_RNA-Seq
GSM4023673 DGM-10159_M12_sm_RNA-Seq
GSM4023674 DGM-10284_M12_sm_RNA-Seq
GSM4023675 DGM-10573_M6_sm_RNA-Seq
GSM4023676 DGM-10704_M12_sm_RNA-Seq
GSM4023677 DGM-10731_M12_sm_RNA-Seq
GSM4023678 DGM-10732_M12_sm_RNA-Seq
GSM4023679 DGM-10768_M6_sm_RNA-Seq
GSM4023680 DGM-10808_M12_sm_RNA-Seq
GSM4023681 DGM-10869_M12_sm_RNA-Seq
GSM4023682 DGM-10886_M12_sm_RNA-Seq
GSM4023683 DGM-10901_M3_sm_RNA-Seq
GSM4023684 DGM-10906_M12_sm_RNA-Seq
GSM4023685 DGM-10922_M12_sm_RNA-Seq
GSM4023686 DGM-10986_M12_sm_RNA-Seq
GSM4023687 DGM-11016_M12_sm_RNA-Seq
GSM4023688 DGM-11038_M12_sm_RNA-Seq
GSM4023689 DGM-11066_M12_sm_RNA-Seq
GSM4023690 DGM-11111_M12_sm_RNA-Seq
GSM4023691 DGM-00221_M12_sm_RNA-Seq
GSM4023692 DGM-10358_M0_sm_RNA-Seq
GSM4023693 DGM-10571_M0_sm_RNA-Seq
GSM4023694 DGM-10625_M12_sm_RNA-Seq

+ 26 more — browse all 66 samples with per-sample file links →

Datasets on a similar topic

GSE284387 DNMT1 RFTS mutation alters RNA splicing and drives neurodegeneration via its RNA-binding function [RNA-Seq] 26 samples shares dnmt1
GSE169614 RNA sequencing of control, DNMT1 knockout and DNMT1/L1TD1 double knockout haploid cells (HAP1) 18 samples shares dnmt1
GSE337276 DNA methylation maintenance by DNMT1 is essential for human trophoblast stem cell homeostasis and differentiation [RNA-seq] 15 samples shares dnmt1
GSE264565 Development of a First-in-Class DNMT1/HDAC Inhibitor with Improved Therapeutic Potential and Potentiated Antitumor Immunity 12 samples shares dnmt1
GSE319745 DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A (RNA-seq) 10 samples shares dnmt1
GSE305298 RNA-seq data from human HCT116 wild-type cells, HCT116 DNMT1 hypomorphs, and HCT116 UHRF1 hypomorphs. 8 samples shares dnmt1
GSE298942 Discovery of a Novel DNMT1 Inhibitor with Improved Efficacy in Treating β-Thalassemia [RNA-seq] 8 samples shares dnmt1
GSE183700 The Role of DNMT1 and C/EBPα in the Regulation of CYP11A1 Expression During Syncytialization of Human Placental Trophoblasts 8 samples shares dnmt1

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.