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Label-free melanoma phenotype classification using AI-based morphological profiling

GSE273247 Homo sapiens (human) Single-cell RNA-seq 41 samples Submitted 2025/07/25 Platform GPL30882Platform GPL24676
At a glance

Single-cell RNA-seq · human · 41 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
MelanomaThis series is part of our curated briefing on it.
Samples
41 samples: 20 conditions, mostly in duplicateGroups 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)
Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity. Within tumors, cells coexist in different mutable phenotypes that exhibit differential functional properties and drug responses. The definition of these phenotypic states has been challenging to rigorously define with conventional marker-based methods, and more high-parameter molecular methods are cell-destructive, labor-intensive, and can take days to weeks to obtain a readout.
Read the full summary ↓
To overcome these technical and practical limitations, we utilized the Deepcell platform to perform real-time classification of unlabeled melanoma cells into Melanocytic and Mesenchymal phenotypes. We used 19 patient-derived cell lines with known Melanocytic or Mesenchymal transcription scores to develop the ‘Melanoma Phenotype Classifier’ to phenotype melanoma cells based on morphology alone. A Classifier accuracy of >88% was achieved, and morphology analysis of the images revealed distinct morphotypes for each phenotype, highlighting distinct morphological differences. To further link phenotypic state with multi-dimensional morphological profiles, we performed genetic and chemical perturbations known to shift the phenotypic state. The AI Classifier successfully predicted shifts in phenotype driven by the perturbations. These results further demonstrate how phenotype is linked to distinct morphological changes that are detectable by AI. Lastly, we applied the Melanoma Phenotype Classifier to dissociated biopsy samples, which revealed phenotypic heterogeneity that was confirmed by single cell RNASeq. This work establishes a link between morphology and Melanoma phenotype, and lays the groundwork for the use of morphology as a label-free method of phenotyping viable melanoma cells combined with additional analyses.
This dataset
Open GSE273247 in finder → Find more human RNA-seq datasets → Melanoma 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 GSE273247_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 PRJNA1140860 and SRA study SRP522584. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly in duplicate
130107 ×2 150922 ×2 170314 ×2 991104 ×2 170123_2 ×2 A375_Ctrl_NAK ×2 A375_Vem ×2 SkMel28_Ctrl_DMSO ×2 +13 more

Supports a between-group comparison across 40 samples.

How we read this: 20 replicated groups read from 41 sample titles; they account for 40 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

+ 1 more — browse all 41 samples with per-sample file links →

More Melanoma datasets

Search all human RNA-seq datasets in GEO →

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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.