GEO series

Complementary single-nucleus and single-cell RNA sequencing reveal tumor state evolution and microenvironmental rewiring in NSCLC

GSE310916 Homo sapiens (human) Single-nucleus RNA-seq 18 samples Submitted 2026/10/20 Platform GPL16791

At a glance

Single-nucleus RNA-seq · human · 18 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 18 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.

Summary (written by the submitters)

Single-cell (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide complementary strategies for profiling complex tissues, but differ in RNA content, cell-type representation and transcript coverage. Here, we perform a direct comparison of matched tumor and adjacent normal tissues from patients with non-small cell lung cancer (NSCLC), integrating in-house and public datasets to delineate platform-specific strengths and limitations.

Read the full summary ↓

Both platforms recovered major cellular compartments with high fidelity: scRNA-seq showed greater sensitivity for immune populations, whereas snRNA-seq captured epithelial, endothelial and fibroblast lineages more effectively. Comparative transcriptomic profiling of shared cell types uncovered distinct yet coherent functional programs: snRNA-seq emphasized nuclear RNA processing and proteostasis, while scRNA-seq enriched for mitochondrial respiration and translational pathways. Integrative regulon of alveolar differentiation and tumor progression revealed coordinated transcriptional regulators (FOXP2, STAT3, MEF2A, FOXO3) governing the AT2-to-AT1 trajectory and divergent gene programs associated with AT2-to-malignant transition. Ligand-receptor mapping further uncovered platform-specific intercellular signaling, with snRNA-seq highlighting matrix- and TGF-ß – driven stromal interactions and scRNA-seq revealing angiogeneic and immune-regulatory pathways. Collectively, this study defines complementary modalities for dissecting epithelial dynamics and tumor communication in NSCLC.

This dataset

Open GSE310916 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 GSE310916_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 PRJNA1364773. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 18 more — browse all 18 samples with per-sample file links →

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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.