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Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologies

GSE295527 Homo sapiens (human) Single-cell RNA-seq 52 samples Submitted 2025/05/27 Platform GPL24676Platform GPL30173Platform GPL34281
At a glance

Single-cell RNA-seq · human · 52 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
52 samples: 34 distinct titles, 6 of them repeatedGroups 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
Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologiesNAR genomics and bioinformatics · 2026
Summary (written by the submitters)
The rapid development of updated and new commercially available single-cell transcriptomics platforms is providing a range of experimental options. Cost, sensitivity, throughput, flexibility, and ease of use influence that selection. We performed a comprehensive comparison of single-cell transcriptomic approaches using multiple standardized PBMCs.
Read the full summary ↓
In evaluating seven recently available kits that interrogate whole transcriptome mRNA and two including TCR sequencing, we report on standard single-cell sensitivity metrics including gene saturation, UMI detection, cell recovery, sequencing efficiency, and cell subset resolution. We also discuss workflow throughput, sample requirements, timing, cost, and labor as critical factors to consider based on project goals. In addition to variable experimental constraints imposed by each platform, our findings highlight differences in cell recovery and sensitivity, which we found to significantly influence cell subset resolution. This work provides a basis by which users can balance performance and practical considerations when selecting an RNA single-cell sequencing platform.
Published in
Evaluating the practical aspects and performance of commercial single-cell RNA sequencing technologies
Elz AE, Gratz D, Long A et al. · NAR genomics and bioinformatics 2026 · PMID 41503158 · doi:10.1093/nargab/lqaf215
This dataset
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.

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 GSE295527_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1255131 and SRA study SRP581094. Searching any of these in the dataset finder brings you back here.

Study design
34 distinct titles, 6 of them repeated
Sorted T-cells from PBMC, GEM-X 5' GEX/WT, sa… ×4 Sorted T-cells from PBMC, NextGEM 5' GEX/WT, … ×4 Sorted T-cells from PBMC, Parse v2 GEX/WT, sa… ×4 Sorted T-cells from PBMC, GEM-X 5' VDJ/TCR, s… ×4 Sorted T-cells from PBMC, NextGEM 5' VDJ/TCR,… ×4 Sorted T-cells from PBMC, Parse v2 VDJ/TCR, s… ×4

How we read this: Read from 52 sample titles: 34 distinct titles with little repetition. 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

+ 12 more — browse all 52 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.