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Lentiviral hTERT overexpression extends the lifespan of primary human epidermal melanocytes and reshapes transcriptional programs

GSE338364 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/07/24 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 24 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)
Samples
24 samples: 8 conditions, mostly in triplicateGroups 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)
Lentiviral hTERT overexpression can generate expandable epidermal melanocyte models that preserve closer global similarity to primary melanocytes than melanoma cell lines. Nevertheless, hTERT overexpression and subsequent clonal selection induced broad remodeling of genes and pathways associated with cell-cycle progression, telomere regulation, antiviral immune response, extracellular matrix organization, cell adhesion, dendrite-associated structures, and pigmentation.
Read the full summary ↓
These findings indicate that lentiviral hTERT overexpression extends the lifespan of human epidermal melanocytes but is not functionally neutral. Instead, this process promotes dedifferentiation and compromises key melanocytic functions. Moreover, single-cell cloning appears to impose additional selection pressure and may further drive model drift. hTERT-extended melanocytes should therefore be used cautiously in studies involving pigmentation, adhesion, differentiation, and normal melanocyte physiology.
This dataset
Open GSE338364 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 GSE338364_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 PRJNA1494395 and SRA study SRP717476. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
A-375 ×3 MIX ×3 P6 ×3 SC-1 ×3 SC-3 ×3 SC-4 ×3 SK-MEL-1 ×3 WM-115 ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 of them. 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
Datasets on a similar topic

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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.