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Single-Molecule Barcoding Technology for Single-Cell Genomics

GSE272618 Mus musculus (mouse) Single-cell RNA-seq 140 samples Submitted 2026/07/18 Platform GPL34281Platform GPL21697Platform GPL24625
At a glance

Single-cell RNA-seq · mouse · 140 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
140 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)
Recent advances in barcoding technologies have introduced unprecedented scalability to single-cell genomic experiments. However, most large-scale studies are focused on the generation of reference cell atlases, which prioritize this goal over cost-efficiency and logistics. For large-scale, multi-million-cell studies to be routine in fields such as clinical diagnostics, drug/target discovery, genetic screening, and gene therapy, it is crucial to simplify the single-cell workflow and the generation of barcoding reagents.
Read the full summary ↓
Here, we introduce AmpliDrop, a technology that streamlines single-cell barcoding by initiating the process using inexpensive single-copy barcodes, which are then amplified as part of the library preparation process, rather than relying on pre-synthesized multi-copy barcode pools. This strategy eliminates the need for barcoded beads and complex combinatorial indexing workflows. Additionally, cell compartmentalization is achieved using a conventional electronic pipette or a robotic liquid handler for automation, providing flexibility for any scale and number of samples. AmpliDrop can capture transcriptomes and chromatin accessibility and can also accommodate user-customized modalities for protein profiling, bacterial or viral DNA detection, and genetic perturbations without construct limitations. We showcase the discovery potential of AmpliDrop by investigating the influence of physical perturbation on cell diversity in human cortical organoids, revealing the selective vulnerability of glycolytic radial glia.
This dataset
Open GSE272618 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 GSE272618_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 PRJNA1137817 and SRA study SRP520934. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 conditions, mostly in duplicate
Barnyard_96well ×96 Exp3_organoid_static ×3 Exp3_organoid_orbital ×3 10X_organoids_benchmark ×2 AmpliDrop_organoids_becnhmark ×2 PBMC_operator1_draw2_fresh ×2 PBMC_operator1_draw2_cryo ×2 Exp1_organoid_nonperturbed ×2 +23 more

Supports a between-group comparison across 122 samples.

How we read this: 13 replicated groups read from 140 sample titles; they account for 122 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

+ 100 more — browse all 140 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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