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SIRPa ablation overcomes exhaustion and enhances the antibody- and CAR-mediated antitumor activities of iPSC-derived CAR Macrophages

GSE285814 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2025/01/03 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 27 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
27 samples: 9 conditions, mostly in triplicateGroups 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)
SIRPa-CD47 “don’t eat me” checkpoint axis plays a critical role in defining anti-tumor activities of macrophages within the tumor microenvironment. However, targeting this axis with anti-CD47 antibodies to improve anti-tumor responses in clinical trials has proven challenging. Here, we demonstrated that iPSC-derived macrophages (iMacs) with ablated SIRPa yield a superior antitumor effect in conjunction with cancer-targeted antibodies (Ab) or chimeric antigen receptor (CAR) against a variety of CD47-expressing tumors in vitro.
Read the full summary ↓
Moreover, SIRPA-KO protected macrophages from Ab- or CAR-driven exhaustion, allowing for efficient phagocytosis of tumors after multiple rounds of cancer re-challenges. Ablation of SIRPa in iMacs improved survival of mice grafted ovarian carcinoma and treated with anti-HER2 Abs, while anti-GD2 CAR iMacs with KO SIRPA demonstrated reduction of initial tumor burden in mice with metastatic neuroblastoma xenograft. Overall, our studies support the feasibility of using the iPSC platform to generate SIRPα-ablated iMacs that are resistant to CD47-mediated inhibition for therapeutic applications.
This dataset
Open GSE285814 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 GSE285814_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 PRJNA1206131 and SRA study SRP555147. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42291136) ↗
Study design
9 conditions, mostly in triplicate
WT ×3 WT-SK24 ×3 WT-SK96 ×3 KO ×3 KO-SK24 ×3 KO-SK96 ×3 ITAM ×3 ITAM-SK24 ×3 +1 more

Supports a between-group comparison across 27 samples.

How we read this: 9 replicated groups read from 27 sample titles; they account for 27 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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