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Modification of Titanium-based implant materials by Zn-doped calcium phosphate coatings improves corrosion resistance, biomineralization, antibacterial efficacy and immunocompatibility

GSE275792 Homo sapiens (human) RNA-seq (gene expression) 83 samples Submitted 2025/12/04 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 83 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
83 samples: 21 conditions, mostly with about 4 replicates eachGroups 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
Zn-doped CaP coating equips Ti implants with corrosion resistance, biomineralization, antibacterial and immunotolerant activitiesJournal of advanced research · 2025
Summary (written by the submitters)
Implant-induced chronic inflammation and aseptic implant failure represent serious clinical challenges in orthopedics, dentistry, and reconstructive surgery. Our study aimed to develop an implant material that minimizes acute and chronic inflammation while providing long-term integration properties. We systematically investigated the innate immune responses modulated by the addition of Zn to CaP coatings, using whole transcriptome profiling through RNA sequencing (RNA-Seq).
Published in
Zn-doped CaP coating equips Ti implants with corrosion resistance, biomineralization, antibacterial and immunotolerant activities
Parau AC, Büyüksungur S, Li G et al. · Journal of advanced research 2025 · PMID 39880075 · doi:10.1016/j.jare.2025.01.045
This dataset
Open GSE275792 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 GSE275792_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 PRJNA1153008 and SRA study SRP528974. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39880075) ↗
Study design
21 conditions, mostly with about 4 replicates each
Macrophages, without stimulation + plastic, d… ×4 Macrophages, without stimulation + Ti64_CaP, … ×4 Macrophages, without stimulation + Ti64_CaP_Z… ×4 Macrophages, without stimulation + Ti64_CaP_Z… ×4 Macrophages, without stimulation + Ti64_CaP_Z… ×4 Macrophages, without stimulation + Ti64_CaP_Z… ×4 Macrophages, IFNgamma stimulation + plastic, … ×4 Macrophages, IFNgamma stimulation + Ti64, dono ×4 +13 more

Supports a between-group comparison across 83 samples.

How we read this: 21 replicated groups read from 83 sample titles; they account for 83 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

+ 43 more — browse all 83 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.