GEO series

Histone marks identify novel transcription factors that parse CAR-T cell subset-of-origin, clinical potential and expansion [RNA-seq]

GSE254808 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/08/04 Platform GPL16791

At a glance

RNA-seq (gene expression) · human · 18 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: 18 samples: 6 × RNA_CM_HD vs 6 × RNA_EM_HD vs 6 × RNA_N_HDGroups 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: Histone marks identify novel transcription factors that parse CAR-T subset-of-origin, clinical potential and expansionNature communications · 2024

Summary (written by the submitters)

Chimeric antigen receptor-modified T cell (CAR-T) immunotherapy has revolutionized the treatment of blood cancers. Parsing the genetic underpinnings of T cell precursor quality and subsequent CAR-T efficacy is challenging. RNA-seq informs infused CAR-T state, but the nature of dynamic transcription during activation hinders identification of transiently or minimally expressed genes, such as transcription factors, and over-emphasizes effector and metabolism genes.

Read the full summary ↓

We investigated whether analyses of transcriptionally repressive and permissive histone methylation marks reveal associations with CAR-T potential beyond what is seen by transcriptomic analysis. We assessed human CD8+ T cell naïve, central and effector memory subsets that form the substrate of CAR-T cell products, and CAR-T cells derived from these subsets. We extended these observations into the clinic, by examining CAR-T products from a clinical trial of lymphoma patients (NCT01865617). We report that histone marks provide a rich dataset for identification of genes not apparent by conventional transcriptomics. Histone marks improved identification of T cell subsets, CAR-T manufactured from these subsets, and CAR-T manufactured from central memory cells from healthy donors and patients. Using this discriminative approach, we controlled for clinical factors and identified a factor, KLF7, associated with CAR-T cell expansion in patients. Epigenomic methods are an orthogonal, robust and wide-reaching approach for the assessment of T cell immunotherapeutic quality.

Published in

Histone marks identify novel transcription factors that parse CAR-T subset-of-origin, clinical potential and expansion

Fiorenza S, Zheng Y, Purushe J et al. · Nature communications 2024 · PMID 39333103 · doi:10.1038/s41467-024-52503-2

This dataset

Open GSE254808 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 GSE254808_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 PRJNA1071987 and SRA study SRP487375. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39333103) ↗

Study design

6 × RNA_CM_HD vs 6 × RNA_EM_HD vs 6 × RNA_N_HD

Supports a between-group comparison across 18 samples.

How we read this: 3 replicated groups read from 18 sample titles; they account for 18 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

GSM8058050 RNA_CM_HD1 RNA_CM_HD
GSM8058051 RNA_CM_HD2 RNA_CM_HD
GSM8058052 RNA_CM_HD3 RNA_CM_HD
GSM8058053 RNA_CM_HD5 RNA_CM_HD
GSM8058054 RNA_CM_HD6 RNA_CM_HD
GSM8058055 RNA_CM_HD7 RNA_CM_HD
GSM8058056 RNA_EM_HD1 RNA_EM_HD
GSM8058057 RNA_EM_HD2 RNA_EM_HD
GSM8058058 RNA_EM_HD3 RNA_EM_HD
GSM8058059 RNA_EM_HD5 RNA_EM_HD
GSM8058060 RNA_EM_HD6 RNA_EM_HD
GSM8058061 RNA_EM_HD7 RNA_EM_HD
GSM8058062 RNA_N_HD1 RNA_N_HD
GSM8058063 RNA_N_HD2 RNA_N_HD
GSM8058064 RNA_N_HD3 RNA_N_HD
GSM8058065 RNA_N_HD5 RNA_N_HD
GSM8058066 RNA_N_HD6 RNA_N_HD
GSM8058067 RNA_N_HD7 RNA_N_HD

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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.