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Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3 [HT SS3]

GSE270928 Homo sapiens (human) Single-cell RNA-seq 6415 samples Submitted 2024/07/02 Platform GPL24676Platform GPL30173
At a glance

Single-cell RNA-seq · human · 6415 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
6415 samples: 179 × Donor1_PMA/Ionomycin_HT SS3_Plate1 vs 177 × Donor1_DMSO_HT SS3_Plate1 vs 44 × Donor1_DMSO_HT SS3_Plate2Groups 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 sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3BMC genomics · 2024
Summary (written by the submitters)
We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow via robotic implementation and established best practices to consistently achieve high cell capture efficiency and data quality. In comparison with the 10X platform, HT Smart-seq3 analysis of primary CD4+ T-cells demonstrated superior sensitivity in gene detection and similar capability to capture major cellular heterogeneity upon sufficient scaling up.
Read the full summary ↓
Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified more productive pairs of alpha and beta chains without additional primer design, enabling more comprehensive profiling of TCRs. Collectively, HT Smart-seq3 provides a cost-effective and scalable method for characterization of single-cell transcriptomes and immune repertoires.
Published in
Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3
Chuang HC, Li R, Huang H et al. · BMC genomics 2024 · PMID 39574011 · doi:10.1186/s12864-024-11036-0
This dataset
Open GSE270928 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 GSE270928_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 PRJNA1129044 and SRA study SRP516597. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39574011) ↗
Study design
179 × Donor1_PMA/Ionomycin_HT SS3_Plate1 vs 177 × Donor1_DMSO_HT SS3_Plate1 vs 44 × Donor1_DMSO_HT SS3_Plate2

Supports a between-group comparison across 400 samples.

How we read this: 3 replicated groups read from the first 400 of 6415 sample titles; they account for 400 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

+ 6375 more — browse all 6415 samples with per-sample file links →

Datasets on a similar topic

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