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Single-cell Total-RNA Profiling Unveils Regulatory Hubs of Transcription Factors

GSE202126 Mus musculus (mouse) Single-cell RNA-seq 1089 samples Submitted 2024/05/19 Platform GPL18573Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 1089 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
1089 samples: 5 conditions, mostly with about 150 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.
Paper
Single-cell total-RNA profiling unveils regulatory hubs of transcription factorsNature communications · 2024
Summary (written by the submitters)
Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry only captures mature RNA species to measure gene expressions.
Read the full summary ↓
Here, we develop a one-step total-RNA chemistry-based scRNA-seq method: snapTotal-seq. We benchmarked this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identified the critical regulatory hubs mediating the cell cycle dynamics. We also applied snapTotal-seq to profile the oncogene-induced senescence and identified the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identified a significant portion of genes whose expression changes occurred in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.
Published in
Single-cell total-RNA profiling unveils regulatory hubs of transcription factors
Niu Y, Luo J, Zong C · Nature communications 2024 · PMID 39009595 · doi:10.1038/s41467-024-50291-3
This dataset
Open GSE202126 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 GSE202126_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 PRJNA834766 and SRA study SRP373434. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39009595) ↗
Study design
5 conditions, mostly with about 150 replicates each
HPNE_sc ×150 U2OS_sc ×83 HEK293T_A ×82 HEK293T_B ×40 3T3_sc ×24 HEK293T_B41_M ×1 HEK293T_B42_M ×1 HEK293T_B43_M ×1 +18 more

Supports a between-group comparison across 379 samples.

How we read this: 5 replicated groups read from the first 400 of 1089 sample titles; they account for 379 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

+ 1049 more — browse all 1089 samples with per-sample file links →

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