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A tri-specific antibody engages T and myeloid cells to augment anti-tumor immunity [bulk RNA-seq]

GSE282589 Homo sapiens (human) RNA-seq (gene expression) 33 samples Submitted 2025/09/15 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 33 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
33 samples
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
A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunityNature biomedical engineering · 2026
Summary (written by the submitters)
Immunologically inert tumors often resist immune checkpoint inhibitors due to a scarcity of tumor-specific T cells and the immunosuppressive microenvironment, despite pronounced and functional bystander T cell infiltration. Here, we develop a novel tri-specific T cell engager (Tri-TCE) designed in a unique format to redirect T cells while mitigating immunosuppression.
Read the full summary ↓
Our comprehensive studies utilizing co-culture systems, patient-derived tumor suspensions (PDTSs), patient-derived tumor fragments (PDTFs), and humanized mouse tumor models, demonstrate that Tri-TCE effectively activates T cells and induces tumor cell lysis.
Published in
A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunity
Yang C, Guo S, Ye K et al. · Nature biomedical engineering 2026 · PMID 41372583 · doi:10.1038/s41551-025-01569-4
This dataset
Open GSE282589 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 GSE282589_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1189430 and SRA study SRP546950. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41372583) ↗
Study design
A cohort of 33 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run Patient1 … Patient33 and carry no group information.

Samples in this study
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.