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Dual-axis Rotating Capillary Containers for 3D Culturing of Spheroids

GSE322504 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/03/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
24 samples: 24 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 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)
Spheroids have found broad applications across biomedical engineering, including tissue modeling and drug screening; however, current culturing methods remain limited in terms of structural stability, uniformity, and scalability. To address these challenges, we developed the Dual‑axis Rotating Capillary Containers (DRCC) platform, a contact‑free system for rapid and uniform spheroid formation.
Read the full summary ↓
The DRCC platform consists of an array of capillary‑based containers paired with a dual‑axis rotational unit. The capillary containers efficiently capture and release cell suspensions, while the dual‑axis rotation counteracts gravitational settling and promotes homogeneous self‑assembly into compact spheroids. Using this system, spheroids with tunable diameters ranging from 100 to 1300 µm were formed within 24 h while maintaining cell viability exceeding 95 %. Compared with spheroids produced using commercial systems, DRCC‑cultured spheroids showed markedly enhanced mechanical stability and a higher compressive modulus with 4 times increase, indicative of stronger intercellular cohesion. Notably, they also exhibited enhanced drug‑penetration barriers that more closely mimic in vivo solid tumors, as evidenced by significantly reduced doxorubicin uptake. This behavior correlates with the upregulated secretion of connexins and collagen in DRCC spheroids, as revealed by transcriptomic and immunofluorescence analyses. Furthermore, the platform enabled the construction of complex co‑culture spheroids that recapitulate the spatial architecture of the tumor microenvironment. Together, the DRCC platform offers a robust, efficient, and controllable tool for generating high‑fidelity spheroids, advancing their use in disease modeling, drug screening, and tissue engineering.
This dataset
Open GSE322504 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 GSE322504_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1430233 and SRA study SRP680162. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 24 sample titles: 24 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.

Samples in this study
Datasets on a similar topic

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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.