← BioTransfer GEO Dataset Finder
GEO series

Revealing differential type I and type II IFN responses in different immune cells in various human diseases by a new IFNs transcriptomic atlas

GSE306664 Homo sapiens (human) Single-cell RNA-seq 131 samples Submitted 2026/06/30 Platform GPL24676Platform GPL34281
At a glance

Single-cell RNA-seq · human · 131 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
131 samples: 131 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)
Interferons (IFNs) are key cytokines mediating broad immunological responses in diverse processes including host defense, autoimmune diseases, and cancer. To gain single cell resolution in response to different IFNs in different human immune cells, we generated a new single-cell transcriptomic dataset in various human immune cell subsets by stimulating with IFN-α, IFN-β, IFN-γ, and IFN-λ1.
Read the full summary ↓
We not only found classical IFN stimulated genes (ISGs) highly elevated across multiple immune cell subsets, but each immune cell subset also displayed unique gene sets in response to IFN-α or IFN-γ stimulation. Furthermore, expression fold changes of many ISGs shared between IFN-α and IFN-γ were different, which prompted us to utilize those unique features to generate a new scoring method. To obtain a comprehensive view of type I & II IFN responses from many different human diseases, we applied our tool to several published scRNAseq datasets from patients with known elevated IFN responses. We also applied this to a new cohort of newly diagnosed multiple myeloma (NDMM) patients and ran a cross-sectional and a longitudinal analysis to explore IFN responses in NDMM compared to healthy and NDMM after induction therapies, with or without daratumumab. We found higher circulating protein levels of IFN-α2A, IFN-β, and IFN-γ, and higher type I and type II IFN scores across all circulating immune cells in NDMM. Induction therapies only decreased type I IFN responses, especially in CD14 monocytes, and circulating IFN-α. Both known ISGs and novel cell-type specific ISGs from our ex vivo dataset could be recapitulated in in vivo NDMM. Our work provides insights into immune cell-type specific responses to IFNs and a new level of clarity to dissect different IFN responses from human diseases. With our cloud-based explorers, this new tool can be easily used by many others to further expand human IFN research.
This dataset
Open GSE306664 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 GSE306664_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 PRJNA1303123 and SRA study SRP607467. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
131 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 131 sample titles: 131 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

+ 91 more — browse all 131 samples with per-sample file links →

More Multiple myeloma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.