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Systematic Comparison of 10X Genomics and Parse Single Cell RNA Technologies across PBMC and CD8+ TEMRA Cells

GSE285843 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2026/01/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 14 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
14 samples: 8 × PBMC sorted cell TEMRA, Mini, sublibrary vs 3 × PBMC, human, Dono vs 2 × PBMC, Parse WT, sublibraryGroups 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)
Single-cell RNA sequencing technologies provide insights into gene expression at the cellular level, enabling detailed analysis of cellular heterogeneity. In this study, we systematically compared two scRNA-seq platforms—10X Genomics and Parse Biosciences—using human peripheral blood mononuclear cells (PBMCs) and terminally differentiated effector memory CD8+ T cells (TEMRAs).
Read the full summary ↓
We identified significant differences in gene expression variability and platform-specific biases, such as ribosomal and mitochondrial gene capture. 10X has a bias for shorter genes, while Parse exhibited enhanced detection of longer transcripts. In CD8+ TEMRAs, the expression of key genes related to immune responses were underrepresented in Parse cells compared to 10X cells (e.g. GNLY, PRF1 and GZMB). These findings underscore the need for careful selection of scRNA-seq platforms based on specific research objectives, as platform-specific biases can influence cell type identification and as well as mechanistic insights from derived from gene expression data. Our results provide critical insights for selection of scRNA-seq experimental platform in immunological studies.
This dataset
Open GSE285843 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 GSE285843_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 PRJNA1206194 and SRA study SRP556153. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 × PBMC sorted cell TEMRA, Mini, sublibrary vs 3 × PBMC, human, Dono vs 2 × PBMC, Parse WT, sublibrary

Supports a between-group comparison across 13 samples.

How we read this: 3 replicated groups read from 14 sample titles; they account for 13 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
Datasets on a similar topic

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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.