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Pooled CRISPR screens identify genes and non-coding genomic regions that regulate red blood cell density

GSE308629 Homo sapiens (human) RNA-seq (gene expression) 99 samples Submitted 2026/08/07 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 99 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
99 samples: 22 conditions, mostly with about 5 replicates eachGroups 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)
Genome-wide association studies have identified >1000 loci associated with clinically important red blood cell (RBC) traits, such as hemoglobin content and cell volume. However, few of these associations have been characterized at the molecular level such that most causal genes and variants remain elusive. Here, we performed pooled CRISPR screens in an erythroid cell line to identify genes and regulatory non-coding sequences that control RBC density.
Read the full summary ↓
We perturbed 556 candidate genes and genomic sequences near 2114 GWAS variants. We used a density gradient to quantify the impact of these CRISPR perturbations on cell density. After validation, we found 17 genes and 13 regions near GWAS variants that regulate cell density. Some of these genes have previously been implicated in RBC biology (e.g. ATP2B4, CCND3, EPOR) although many are novel (e.g. CHTF8, CTU2, DNASE2). We confirmed that deletions in the osmotic stress response kinase gene OXSR1 increase cell density, and a phosphoproteome analysis in OXSR1-depleted cells indicated that this phenotype is accompanied with a dephosphorylation of the upstream kinase WNK1 and the downstream target KCC3 (SLC12A6). We also combined CRISPR perturbations and RNA-sequencing to show how a non-coding genomic sequence near rs13255015 regulates the expression of the transcription factor ZFAT in cis and SLC4A1 in trans. SLC4A1 encodes Band3, a known regulator of RBC hydration and volume. Our results suggest experimental strategies to characterize GWAS findings and provide new molecular insights into the regulation of complex RBC traits.
This dataset
Open GSE308629 in finder → Find more human 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 GSE308629_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 PRJNA1331747 and SRA study SRP622532. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
22 conditions, mostly with about 5 replicates each
DS_Cas9_R1_fraction ×5 DS_Cas9_R2_fraction ×5 DS_Cas9_R3_fraction ×5 DS_Cas9_R4_fraction ×5 DS_KRAB_R1_fraction ×5 DS_KRAB_R2_fraction ×5 DS_KRAB_R3_fraction ×5 DS_KRAB_R4_fraction ×5 +17 more

Supports a between-group comparison across 96 samples.

How we read this: 22 replicated groups read from 99 sample titles; they account for 96 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

+ 59 more — browse all 99 samples with per-sample file links →

Datasets on a similar topic

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