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AMAR-seq: automated multimodal sequencing of DNA methylation, chromatin accessibility, and RNA expression with single-cell resolution

GSE261788 Mus musculus (mouse) Single-cell RNA-seq 83 samples Submitted 2024/03/21 Platform GPL24247Platform GPL24676
At a glance

Single-cell RNA-seq · mouse · 83 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
83 samples: 14 conditions, mostly with about 6 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)
Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility and RNA sequencing on a chip with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods.
Read the full summary ↓
The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas, implement single-cell copy number variation analysis, and construct a single-gene, single-base resolution gene expression, DNA methylation, and chromatin accessibility landscape. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome which may contribute to the unravelling of the molecular mechanisms of early embryonic development. Collectively, these results illustrate that the employment of AMAR-seq can deeply and accurately establish single-cell multi-omics regulatory networks in a single-cell context in a cost-efficient and automated manner, which paves the way for incisive dissection of complex life procedures.
This dataset
Open GSE261788 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 GSE261788_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 PRJNA1089071 and SRA study SRP496016. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly with about 6 replicates each
AMAR_nucleus ×12 AMAR_RNA ×12 mESC_3d_nucleus ×7 mESC_3d_RNA ×7 mESC_0d_nucleus ×6 mESC_6d_nucleus ×6 mESC_0d_RNA ×6 mESC_6d_RNA ×6 +6 more

Supports a between-group comparison across 83 samples.

How we read this: 14 replicated groups read from 83 sample titles; they account for 83 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

+ 43 more — browse all 83 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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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.