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Multimodal single-cell and spatial profiling reveals altered T cell-mediated immunity and B-cell follicular architecture in non-metastatic lymph nodes of patients with aggressive non-small cell lung cancer

GSE316782 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2026/01/21 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 18 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
18 samples: 18 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)
Regional lymph nodes (LNs) in the thoracic cavity serve as essential immunological hubs that coordinate humoral and cell-mediated responses against the development and progression of non-small cell lung cancer (NSCLC). To investigate immune dysregulation in the non-metastatic regional LNs of patients with aggressive NSCLC, we performed multimodal profiling on 36 LNs from 11 patients undergoing curative-intent resection including CITE-seq, scRNA-seq, and Imaging Mass Cytometry (IMC).
Read the full summary ↓
Regional N1 LNs from patients with more aggressive disease (stage IB–IIIA) exhibited a significant enrichment of dysfunctional CD8⁺ T cells and regulatory T cells (Tregs) compared to N2 LNs and LNs from patients with less aggressive disease (stage IA). These immune subsets were spatially co-localized with mature regulatory dendritic cells (mregDCs; CD1c⁺, TIM3⁺, LAMP3⁺), forming an immunosuppressive niche uniquely enriched in the N1 LNs of higher-stage patients. Concurrently, higher-stage N1 LNs contained larger number of “decorticated” B-cell follicles characterized by decreased encapsulation of the mantle zone layer surrounding the germinal centers. This mantle zone disorganization was associated with increased spatial niches involving Tregs, CD68⁺ CD163⁺ TIM3⁺ Macrophages, CD163⁺ TIM3dim Monocytic-Myeloid Derived Suppressor Cells (M-MDSC), plasma B cells, and a decrease in spatial niches involving CD4⁺ T helper cells and fibroblastic reticular cells (FRCs). Together, our findings reveal parallel alterations in humoral and cell-mediated immunity within the regional LNs of patients with aggressive NSCLC.
This dataset
Open GSE316782 in finder → Find more human RNA-seq datasets → Lung cancer 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 GSE316782_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 PRJNA1404196 and SRA study SRP663644. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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
More Lung cancer datasets

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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.