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Targeting non-junctional Claudin-1 with monoclonal antibodies to treat hepatocellular carcinoma [scRNA-seq]

GSE196392 Homo sapiens (human) Single-cell RNA-seq 2 samples Submitted 2025/01/29 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 2 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)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
2 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)
Abstract: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Despite new treatment approvals, treatment response and prognosis of patients with advanced HCC remains poor. Claudin-1 (CLDN1) is a membrane protein expressed in tight junctions but also non-junctionally at the basolateral membrane of hepatocytes.
Read the full summary ↓
While the function of CLDN1 within tight junctions is well characterized, the role of non-junctional (NJ) CLDN1 in HCC remains unexplored. Here we show that targeting NJ-CLDN1 with a humanized monoclonal antibody (mAb) suppresses tumor growth in different pre-clinical models. Mechanistic studies including single cell RNA sequencing of multicellular patient tumorspheres suggest that non-junctional CLDN1 regulates tumor stemness, metabolism and oncogenic signaling with impact on the tumor immune microenvironment. Our results provide the rationale for targeting NJ-CLDN1 in HCC and pave the way to novel therapeutic interventions with NJ-CLDN1 mAbs aimed at improving the limited efficacy of current therapies.
This dataset
Open GSE196392 in finder → Find more human RNA-seq datasets → Hepatocellular carcinoma 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 GSE196392_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 PRJNA804572 and SRA study SRP358972. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Samples in this study

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