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CAR T Cells Secreting NGF-Neutralizing scFv Enhance Efficacy in Clear Cell Renal Cell Carcinoma by Relieving Immunosuppression through Immunosympathectomy [scRNA-seq]

GSE279394 Mus musculus (mouse) Single-cell RNA-seq 3 samples Submitted 2024/12/18 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 3 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
3 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
CAR T cells secreting NGF-neutralizing scFv enhance efficacy in clear cell renal cell carcinoma by relieving immunosuppression through immunosympathectomyJournal for immunotherapy of cancer · 2024
Summary (written by the submitters)
This study investigated the role of sympathetic denervation in enhancing CAR T cell anti-tumor efficacy in renal clear cell carcinoma. By blocking the NGF pathway with antibodies, we demonstrated reduced sympathetic nerve distribution and delayed tumor progression. Furthermore, CAR T cells engineered to secrete NGF scFv achieved tumor immunosympathectomy, leading to improved anti-tumor effects.
Read the full summary ↓
RNA sequencing revealed that this enhancement was linked to reduced terminal exhaustion in CD8 T cells and inhibited macrophage polarization from M1 to M2, promoting a robust anti-tumor immune state. Additionally, splenic T cells exhibited a stronger immune effector phenotype following the infusion of NGF scFv-secreting CAR T cells. These findings suggest that immunosympathectomy may represent a novel strategy to mitigate tumor-induced immunosuppression and improve CAR T cell efficacy in solid tumors.
Published in
CAR T cells secreting NGF-neutralizing scFv enhance efficacy in clear cell renal cell carcinoma by relieving immunosuppression through immunosympathectomy
Yang P, Chen X, Yu F et al. · Journal for immunotherapy of cancer 2024 · PMID 39653553 · doi:10.1136/jitc-2024-009910
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 GSE279394_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 PRJNA1172631 and SRA study SRP538419. Searching any of these in the dataset finder brings you back here.

Samples in this study

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