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Independent cell type-specific expression and distal regulation of the 9p21 locus cell cycle regulators: p14ARF, p16INK4A, p15INK4A, and ANRIL [CRISPR and scRNA-seq]

GSE309511 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2026/06/29 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
32 samples: 16 × CRISPRa screen and dial-up scRNA-seq, WI-38 fibroblasts, array vs 16 × CRISPRa screen mRNA scRNA-seq, WI-38 fibroblasts, arrayGroups 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 9p21 locus encodes tumor suppressors p16INK4A and p14ARF (both encoded by CDKN2A), p15INK4B (CDKN2B), and a long non-coding RNA, ANRIL (CDKN2B-AS1). The ~1 megabase locus is notable for a high density of single nucleotide polymorphisms (SNPs) associated with aging-related diseases and traits. Despite clear importance, our understanding of the the cell-type specific expression dynamics and cis-regulatory mechanisms of the 9p21 transcripts has been constrained by the complexity of the transcript structures, low expression, and severe alterations to the locus in cancer cell lines.
Read the full summary ↓
Here, we innovated custom computational and molecular tools to overcome these technical challenges and fill critical knowledge gaps in our understanding of 9p21 locus architecture and regulation of the 9p21 transcripts. Across tissues, we found that p15INK4b is the predominant transcript. We deconvolved the expression of the CDKN2A transcripts, p16INK4A and p14ARF, revealing stark differences in the expression of these overlapping but functionally distinct transcripts across tissues and cell types. Comparative analysis in a model of fibroblast senescence revealed a striking switch from preferential p14ARF expression in cycling cells, to p16INK4A and p15INK4b as cells age and senescence. Perturbation of putative cis regulatory elements nominated a network of promoters and enhancers that regulate p16INK4A, p14ARF, and p15INK4b and ANRIL. The identified elements are accessible in vivo, and can be used to guide studies into variant interpretation. Our systematic characterization of 9p21 transcript isoforms, promoters, and distal elements across cell states, in vivo tissues and senescence offers new mechanistic insights and a framework for future studies of this vital but poorly understood locus.
This dataset
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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 GSE309511_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 PRJNA1336192 and SRA study SRP629597. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 × CRISPRa screen and dial-up scRNA-seq, WI-38 fibroblasts, array vs 16 × CRISPRa screen mRNA scRNA-seq, WI-38 fibroblasts, array

Supports a between-group comparison across 32 samples.

How we read this: 2 replicated groups read from 32 sample titles; they account for 32 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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