GEO series

Simultaneous profiling of full-length RNA transcripts and chromatin accessibility within single cells of human retinal organoids

GSE251754 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/07/13 Platform GPL28352Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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: 10 samples: 6 × scRCAT_seq2_ES_SIRVGroups 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: Simultaneous profiling of RNA isoforms and chromatin accessibility of single cells of human retinal organoidsNature communications · 2024

Summary (written by the submitters)

Single-cell multi-omics sequencing can integrate transcriptome and epigenome to analyze the complex mechanisms underlying neuron development and regeneration, but most current methods are based on second-generation short-read sequencing, which has low efficiency in detecting RNA structural heterogeneity. Long-length sequencing can analyze RNA structures, but the throughput and the number of transcripts detected at the single-cell level are very low, and single-cell level epigenome profiling has not been accomplished either.

Read the full summary ↓

Therefore, there is currently a lack of an effective method that can integrate RNA splicing and epigenetic modification to analyze the molecular mechanism of neural development. This study developed a single-cell multi-omics assay based on short-read sequencing for the simultaneous detection of single-cell full-length RNA isoforms and DNA accessibility. The accuracy of its resolution in RNA transcript structure can reach 94.5%, and the sensitivity of detecting single-cell gene expression is twice that of third-generation sequencing. And it can detect over 10,000 single nuclei at one run, enabling the effective integrated analysis of single-cell RNA isoforms and DNA accessibility at high throughput. We used this method to construct a multidimensional cell atlas of human retinal organoids, and found that gene expression and differential choices of isoforms of multiple fate-determining factors were significantly associated with chromatin accessibility. This method provides a new technical method for dissecting the multidimensional molecular mechanism of fate determination in neural cell development and regeneration.

Published in

Simultaneous profiling of RNA isoforms and chromatin accessibility of single cells of human retinal organoids

Zhang S, Xiao Y, Mo X et al. · Nature communications 2024 · PMID 39271703 · doi:10.1038/s41467-024-52335-0

This dataset

Open GSE251754 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 GSE251754_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 PRJNA1055164 and SRA study SRP479266. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39271703) ↗

Study design

6 × scRCAT_seq2_ES_SIRV

Supports a between-group comparison across 6 samples.

How we read this: 1 replicated groups read from 10 sample titles; they account for 6 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

GSM7987368 scRCAT_seq2_ES_SIRV_1 scRCAT_seq2_ES_SIRV
GSM7987369 scRCAT_seq2_ES_SIRV_2 scRCAT_seq2_ES_SIRV
GSM7987370 scRCAT_seq2_ES_SIRV_3 scRCAT_seq2_ES_SIRV
GSM7987371 scRCAT_seq2_ES_SIRV_4 scRCAT_seq2_ES_SIRV
GSM7987372 scRCAT_seq2_ES_SIRV_5 scRCAT_seq2_ES_SIRV
GSM7987373 scRCAT_seq2_ES_SIRV_6 scRCAT_seq2_ES_SIRV
GSM7987374 SclSOr_seq_ES_SIRV
GSM7987375 scRCAT_seq2_D45_1
GSM7987376 D45_cDNA_Library
GSM7987377 D45_ATAC_Library

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