GEO series

Integration of Alzheimer’s GWAS, 3D genomics, and single-cell CRISPRi non-coding screen implicates causal variants in a microglial enhancer regulating TSPAN14. [RNA-Seq]

GSE293119 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/05/26 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 18 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: 18 samples: 6 conditions, mostly in triplicateGroups 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.
Paper: Integrative genomics and single-cell CRISPRi screening dissect Alzheimer GWAS non-coding variants regulating TSPAN14American journal of human genetics · 2026

Summary (written by the submitters)

While GWAS have been successful in providing variant-to-trait associations for human complex diseases, functional dissection of the discovered loci has lagged behind. Here, we describe a variant-to-gene (V2G) mapping effort for Alzheimer’s disease (AD) to implicate causal variants and effector genes from the most recent AD GWAS meta-analyses (101 loci).

Read the full summary ↓

We leveraged our genomics datasets comprising high-resolution promoter Capture C, ATAC-seq, and RNA-seq from brain-relevant cell types to fine-map AD GWAS variants, identifying 89 candidate causal SNPs and 69 effector genes. We then designed a single-cell CRISPRi screen to perturb candidate regulatory regions (n=74) and assess the transcriptional response in the human microglial cell line, HMC3. Our screen across ~97,000 cells identified 19 regulatory regions and 19 effector genes. We then elected to functionally dissect our top hit, the TSPAN14 locus, and we show that an intronic region containing AD-associated SNPs rs7080009, rs1870138, and rs1870137 is a microglia-specific enhancer, with the AD risk haplotype increasing its activity. CRISPR precise genomic deletion of this region decreases TSPAN14 expression, alters specific cellular pathways including cell adhesion, and decreases secreted levels of pro-inflammatory cytokines IL-6 and IL-8, which are known biomarkers of aging and AD. Our work provides a systematic framework to map GWAS signals to their effector genes for AD and other brain-related disorders, and provides robust leads to follow up with in-depth functional investigations.

Published in

Integrative genomics and single-cell CRISPRi screening dissect Alzheimer GWAS non-coding variants regulating TSPAN14

Laub S, Tulina N, Hoffman M et al. · American journal of human genetics 2026 · PMID 42190655 · doi:10.1016/j.ajhg.2026.04.011

This dataset

Open GSE293119 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 GSE293119_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 PRJNA1242839 and SRA study SRP573765. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42190655) ↗

Study design

6 conditions, mostly in triplicate

HMC3 KO clone 1, ×3 HMC3 KO clone 2, ×3 HMC3 empty vector, ×3 HMC3 control NTC1, ×3 HMC3 control NTC2, ×3 HMC3 control NTC3, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM8876721 HMC3 KO clone 1, rep1 HMC3 KO clone 1,
GSM8876722 HMC3 KO clone 1, rep2 HMC3 KO clone 1,
GSM8876723 HMC3 KO clone 1, rep3 HMC3 KO clone 1,
GSM8876724 HMC3 KO clone 2, rep1 HMC3 KO clone 2,
GSM8876725 HMC3 KO clone 2, rep2 HMC3 KO clone 2,
GSM8876726 HMC3 KO clone 2, rep3 HMC3 KO clone 2,
GSM8876727 HMC3 empty vector, rep1 HMC3 empty vector,
GSM8876728 HMC3 empty vector, rep2 HMC3 empty vector,
GSM8876729 HMC3 empty vector, rep3 HMC3 empty vector,
GSM8876730 HMC3 control NTC1, rep1 HMC3 control NTC1,
GSM8876731 HMC3 control NTC1, rep2 HMC3 control NTC1,
GSM8876732 HMC3 control NTC1, rep3 HMC3 control NTC1,
GSM8876733 HMC3 control NTC2, rep1 HMC3 control NTC2,
GSM8876734 HMC3 control NTC2, rep2 HMC3 control NTC2,
GSM8876735 HMC3 control NTC2, rep3 HMC3 control NTC2,
GSM8876736 HMC3 control NTC3, rep1 HMC3 control NTC3,
GSM8876737 HMC3 control NTC3, rep2 HMC3 control NTC3,
GSM8876738 HMC3 control NTC3, rep3 HMC3 control NTC3,

Datasets on a similar topic

GSE290367 Single-cell multiomic integration identifies widespread, cell-type resolved fetal reactivation in the diseased human heart 90 samples shares integration, single-cell
GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell
GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
GSE327167 Single-cell RNA-seq profiling of pleural mesothelioma and comparator pleural specimens 113 samples shares single-cell
GSE266265 Endometrial Insights: Unmasking Endometriosis through Single-Cell Profiling and AI-Based Prediction 60 samples shares single-cell

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.