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Small cell lung cancer induces synaptic scaling to alter neuronal excitability

GSE305403 Homo sapiens (human) RNA-seq (gene expression) 3 samples Submitted 2025/10/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 3 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)
Topic
Small cell lung cancerThis series is part of our curated briefing on it.
Samples
3 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)
Tumor cell plasticity in novel microenvironments is central to the integration and subsequent growth of metastatic cells. However, the functional consequences of tumor cell integration with central neurons remains understudied. Here, we address this question using small cell lung cancer (SCLC), which has an extraordinary propensity to metastasize to the brain in humans.
Read the full summary ↓
Transcriptomic and electrophysiological analysis of SCLC cells in neuronal microenvironments reveal a predilection for GABAergic synapse formation between neurons and a subset of adjacent SCLC cells. This functional integration of SCLC with central neurons induces multiplicative synaptic upscaling and dysregulated neuronal excitability. Aberrant excitation in human neurons with SCLC is sustained by synaptic NMDA receptor activation and can be reduced by the FDA approved NMDA receptor blocker memantine. These findings reveal strategies to normalize tumor-induced exacerbation of aberrant neuronal activity.
This dataset
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.

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 GSE305403_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1305467 and SRA study SRP608764. Searching any of these in the dataset finder brings you back here.

Samples in this study

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