Single-cell atlas of patient-derived cervical organoids uncovers epithelial immune heterogeneity and intercellular crosstalk during Chlamydia infection
Single-cell RNA-seq · human · 14 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
- 14 samples: 14 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed expression table (series matrix) and the array files, from NCBI.
- Paper
- Single-cell atlas of cervical organoids uncovers epithelial immune heterogeneity and intercellular cross-talk during Chlamydia infectionScience advances · 2025
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 GSE231781_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 PRJNA967668 and SRA study SRP436147. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 14 sample titles: 14 distinct titles with little repetition. 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.
- GSM7300256 Ecto-3D uninfected, 36hpi
- GSM7300257 Ecto-3D Ctr infected, 36hpi
- GSM7300258 Ecto-3D Ctr infected FACS-sorted, 36hpi
- GSM7300259 Endo-3D uninfected, 36hpi
- GSM7300260 Endo-3D Ctr infected, 36hpi
- GSM7300261 Endo-3D Ctr infected FACS-sorted, 36hpi
- GSM7300298 Endocervical 3D organoids_5days post_Uninfected_Replicate1 Patient-2 Uninfected
- GSM7300299 Endocervical 3D organoids_5days post_Chlamydia trachomatis_Infection_Replicate1 Patient-2 Ctr Infected
- GSM7300300 Endocervical 3D organoids_5days post_Uninfected_Replicate2 Patient-2 Uninfected
- GSM7300301 Endocervical 3D organoids_5days post_Chlamydia trachomatis_Infection_Replicate2 Patient-2 Ctr Infected
- GSM7300302 Endocervical 3D organoids_5days post_Uninfected_Replicate1 Patient-3 Uninfected
- GSM7300303 Endocervical 3D organoids_5days post_Chlamydia trachomatis_Infection_Replicate1 Patient-3 Ctr Infected
- GSM7300304 Endocervical 3D organoids_5days post_Uninfected_Replicate2 Patient-3 Uninfected
- GSM7300305 Endocervical 3D organoids_5days post_Chlamydia trachomatis_Infection_Replicate2 Patient-3 Ctr Infected
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares heterogeneity, single-cell
- GSE294559 Single-cell multi-omics characterize colorectal tumors, adjacent healthy tissue and matched organoids identifying CRC-unique features 72 samples shares single-cell, organoids
- GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares single-cell, organoids
- GSE220774 Side- and disease-dependent changes in human aortic valve cell population and transcriptomic heterogeneity determined by single-cell RNA sequencing 15 samples shares heterogeneity, single-cell
- GSE303509 Biomarker-guided responses in patient-derived organoids predict effective therapies in breast cancer 14 samples shares patient-derived, organoids
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares heterogeneity
- GSE327101 Single-Cell Multimodal Atlas Reveals Regulatory Programs in the Human Brain 220 samples shares single-cell
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.