GEO series

NKG2D-mediated cytotoxicity of CD4 cytotoxic T cells in multiple myeloma

GSE277428 Homo sapiens (human) Single-cell RNA-seq 27 samples Submitted 2025/12/01 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 27 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Multiple myelomaThis series is part of our curated briefing on it.
Samples: 27 samples: 27 samples, each with a distinct title — no replicated groupsGroups 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)

Emerging evidence indicates that CD4+ T cells contribute to antitumor immunity beyond their traditional roles as helpers or regulators. However, the specific subset of CD4+ T cells mediating beneficial outcomes in patients with multiple myeloma remains unclear. Here, we performed single-cell RNA sequencing and T cell receptor sequencing on CD4+ T cells sorted from the bone marrow of patients across the stages of myeloma progression.

Read the full summary ↓

We identified several distinct states of CD4+ cytotoxic T lymphocytes (CTLs) that were significantly increased and clonally expanded in myeloma patients. CD4+ CTLs displayed transcriptional and phenotypic characteristics indicative of cytotoxicity, demonstrating their ability to directly kill myeloma cells. This cytotoxicity, however, was abrogated by NKG2D blockade. Notably, the abundance of NKG2D+CD4+ CTLs correlated with improved survival in myeloma patients. Our findings suggest that harnessing CD4+ CTLs could lead to novel strategies for enhancing immunotherapy outcomes in multiple myeloma.

This dataset

Open GSE277428 in finder → Find more human RNA-seq datasets → Multiple myeloma landscape →

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 GSE277428_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 PRJNA1162143 and SRA study SRP533231. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

27 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 27 sample titles: 27 distinct titles with little repetition. 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

GSM8522310 GEX_BM1_415_503_506_528
GSM8522311 GEX_BM10_480_483_1_3
GSM8522312 GEX_BM2_400_500_600_700
GSM8522313 GEX_BM3_800_504_5_7
GSM8522314 GEX_BM4_8_10_442_477
GSM8522315 GEX_BM5_489_450_465_470
GSM8522316 GEX_BM6_471_526_476_469
GSM8522317 GEX_BM7_476_469_480_483
GSM8522318 GEX_BM8_2_513_460_416
GSM8522319 GEX_BM9_455_479_491_4
GSM8522320 TCR_BM1_415_503_506_528
GSM8522321 TCR_BM10_480_483_1_3
GSM8522322 TCR_BM2_400_500_600_700
GSM8522323 TCR_BM3_800_504_5_7
GSM8522324 TCR_BM4_8_10_442_477
GSM8522325 TCR_BM5_489_450_465_470
GSM8522326 TCR_BM6_471_526_476_469
GSM8522327 TCR_BM7_476_469_480_483
GSM8522328 TCR_BM8_2_513_460_416
GSM8522329 TCR_BM9_455_479_491_4
GSM8828917 pooling of sorted CD4+ T cells obtained from Healthy_2, SMM_1, MGUS_1
GSM8828918 pooling of sorted CD4+ T cells obtained from SMM_2, Healthy_3, MGUS_2
GSM8828919 pooling of sorted CD4+ T cells obtained from SMM_3, Healthy_4, MGUS_3
GSM8828920 pooling of sorted CD4+ T cells obtained from Healthy_5, SMM_4, MGUS_4
GSM8828921 pooling of sorted CD4+ T cells obtained from BM848, BM720, BM668
GSM8828922 pooling of sorted CD4+ T cells obtained from BM694, BM37, BM678-1
GSM8828923 pooling of sorted CD4+ T cells obtained from BM474-1, BM_900, BM33

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GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma
GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma
GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma
GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma

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