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Oligo-CALL: A Next-Generation Barcoding Platform for Studying Resistance to Targeted Therapy [bulk RNA-seq]

GSE299096 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2025/10/06 Platform GPL34284
At a glance

Single-cell RNA-seq · human · 48 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
48 samples: 16 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)
Deconvolving heterogeneous cell populations and tracing individual clonal trajectories are essential for unraveling complex biological processes, such as therapy resistance. Although CRISPR-assisted cellular barcoding holds promises for lineage tracing, current platforms exhibit limitations underscored by suboptimal efficiencies and incompatibilities with single-cell transcriptomics.
Read the full summary ↓
We introduce Oligo-CALL (Oligonucleotide-induced CRISPRa-Assisted Lineage Labeling), a new cellular barcoding system enabling precise tumor clone tracking, convenient live-clone isolation without additional genomic modification, and compatibility with single-cell RNA sequencing. Applying Oligo-CALL to human lung cancer cells revealed key insights into the resistance to KRAS G12C inhibitors (G12Ci). Clonal fate assays demonstrated that certain clones were repeatedly enriched under G12Ci pressure, indicating a “predestined” rather than “stochastic” mode of resistance. Paired functional assays of treatment-naïve versus resistant clones revealed clone-specific, acquired resistance phenotypes, manifesting as transient or permanent. Single-cell transcriptomics confirmed the activation of diverse yet clone-specific adaptive pathways underlying G12Ci resistance. Together, Oligo-CALL offers a powerful way to investigate the evolution of resistance to targeted therapies.
This dataset
Open GSE299096 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 GSE299096_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 PRJNA1272565 and SRA study SRP590166. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in triplicate
H358 cells, Clone#1, treatment-naïve, vehicle… ×3 H358 cells, Clone#1, treatment-naïve, AMG510 … ×3 H358 cells, Clone#1, AMG-resistant, Vehicle t… ×3 H358 cells, Clone#1, AMG-resistant, AMG510 tr… ×3 H358 cells, Clone#2, treatment-naïve, vehicle… ×3 H358 cells, Clone#2, treatment-naïve, AMG510 … ×3 H358 cells, Clone#2, AMG-resistant, Vehicle t… ×3 H358 cells, Clone#2, AMG-resistant, AMG510 tr… ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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.