← BioTransfer GEO Dataset Finder
GEO series

In vivo BMAL1 occupancy mapping using MACS-Calling Cards reveals disease-associated retargeting in Cln3Δex7/8 astrocytes

GSE318942 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 65 samples Submitted 2026/05/01 Platform GPL34284Platform GPL16417Platform GPL34290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 65 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
65 samples: 13 conditions, mostly in duplicateGroups 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)
Astrocytes maintain neuronal homeostasis through cellular programs that are frequently disrupted early in neurodegenerative disease, many of which are regulated by the circadian clock. The core clock transcription factor BMAL1 is required for normal astrocyte function, yet whether its regulatory activity is altered during neurodegeneration remains unclear.
Read the full summary ↓
Moreover, astrocyte circadian gene expression patterns are profoundly reprogrammed in disease, yet the underlying mechanisms remain unclear, primarily due to a lack of methods for identifying DNA binding sites of BMAL1 in specific cell types in the brain. Here, we have developed an in vivo strategy to record transcription factor genomic occupancy specifically in astrocytes (MACS-Calling Cards, MACS-CC), and used it to map BMAL1 binding. We applied this strategy to the Cln3Δex7/8 mouse model of CLN3 disease, a fatal neurodegenerative disorder, which exhibits early astrocyte dysfunction prior to widespread neuronal loss as well as circadian dysregulation. This allowed us to test how the progressive pathological cascade caused by CLN3 deficiency reprograms BMAL1 binding in astrocytes. BMAL1 binding was extensively redistributed, with comparable numbers of wild-type-specific and disease-specific sites. Wild-type-specific BMAL1 binding was enriched near genes involved in circadian regulation and astrocyte differentiation, whereas disease-specific sites lacked coherent functional enrichment. Consistent with these changes, RNA-seq of sorted astrocytes revealed reduced expression of mature astrocyte markers in Cln3Δex7/8 mice. To define the mechanisms underlying BMAL1 retargeting in Cln3Δex7/8 astrocytes, we tested whether changes in chromatin accessibility could explain disease-associated gains and losses of BMAL1 binding. Although chromatin accessibility was broadly remodeled, differential accessibility did not predict BMAL1 binding gains or losses. Instead, motif analyses supported a model in which loss of normal cooperative transcription factor partnerships contributes to BMAL1 retargeting. Together, these findings demonstrate that MACS-CC enables in vivo transcription factor occupancy mapping in astrocytes and reveals the mechanisms behind early rewiring of circadian regulatory programs within a model of progressive pathology leading to neurodegeneration. Our data also shed new light on astrocyte dysfunction in CLN3 disease.
This dataset
Open GSE318942 in finder → Find more 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 GSE318942_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1420848 and SRA study SRP675884. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 conditions, mostly in duplicate
cc_022525_wt_bmal1_male ×4 cc_022725_wt_bmal1_female ×4 cc_122024_wt_bmal1_female ×4 cc_022425macs_wt_unfused_male ×4 cc_112024macs_cln3_bmal1_female ×3 cc_032025macs_cln3_unfused_female ×3 cc_122824_wt_bmal1_male ×3 cc_022825macs_wt_unfused_female ×3 +32 more

Supports a between-group comparison across 38 samples.

How we read this: 13 replicated groups read from 65 sample titles; they account for 38 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

+ 25 more — browse all 65 samples with per-sample file links →

Popular RNA-seq datasets

No close match found for this topic, so these are simply the most-viewed datasets of the same kind.

Search all 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.