Single-cell transcriptomic profiling reveals the heterogeneity of miR-18a-mediated resistance to targeted therapy and immunotherapy in melanoma
Single-cell RNA-seq · human · 12 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)
- Samples
- 12 samples: 8 × Single-cell sequencing WT vs 2 × scRNA-seq after miR-18a KOGroups 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.
- Paper
- Genome-Wide CRISPR Screen Identifies a microRNA Orchestrating Pleiotropic Resistance to Targeted Therapy and T Cell Immunity in MelanomaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE318353_RAW.tar (it can run to tens of gigabytes).
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: 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 PRJNA1223135 and SRA study SRP563658. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 2 replicated groups read from 12 sample titles; they account for 10 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.
- GSM9493096 Single-cell sequencing WT 1 Single-cell sequencing …
- GSM9493097 Single-cell sequencing WT 2 Single-cell sequencing …
- GSM9493098 Single-cell sequencing WT 3 Single-cell sequencing …
- GSM9493099 Single-cell sequencing WT 4 Single-cell sequencing …
- GSM9493100 Single-cell sequencing WT 5 Single-cell sequencing …
- GSM9493101 Single-cell sequencing WT 6 Single-cell sequencing …
- GSM9493102 Single-cell sequencing WT 7 Single-cell sequencing …
- GSM9493103 Single-cell sequencing WT 8 Single-cell sequencing …
- GSM9493104 scRNA-seq, TR
- GSM9493105 scRNA-seq, VR
- GSM9493106 scRNA-seq after miR-18a KO 1 scRNA-seq after miR-18a…
- GSM9493107 scRNA-seq after miR-18a KO 2 scRNA-seq after miR-18a…
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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.