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Construction and Evaluation of an Acid-Responsive Antibacterial Coating with Laser-Induced Micro/Nano-Structured Titanium Surface Loaded LAMA3 pDNA@ZIF-8 Nanoparticles for Enhanced Soft Tissue Integration

GSE325639 Homo sapiens (human) RNA-seq (gene expression) 9 samples Submitted 2026/03/25 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 9 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
9 samples: 9 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)
Unlike natural teeth, dental implants exhibit distinct peri-implant structures characterized by only about one-third of the hemidesmosomal attachments and the absence of periodontal ligament fiber insertion, which compromise their long-term success. Guided epithelial ingrowth via laser-induced microgrooves combined with enhanced hemidesmosome formation through LAMA3 gene transfection represents a promising strategy to improve soft tissue integration.
Read the full summary ↓
In this study, a micro/nano-textured surface was fabricated on titanium implants by laser patterning followed by anodization to promote directional cell growth. A layer-by-layer self-assembled coating composed of ZIF-8-encapsulated LAMA3 plasmid was constructed on this micro/nano-surface. This coating exhibited acid-responsive release behavior and demonstrated antibacterial activity. More importantly, it effectively delivered the LAMA3 plasmid into cells and upregulated the expression of hemidesmosome-related proteins integrin α6 and integrin β4, both in vitro and in a rat implantation model. RNA sequencing revealed that the coating modulated global gene expression profiles and subsequently influenced cellular functions. In immortalized keratinocytes, differentially expressed genes were enriched in the MAPK signaling pathway, which was further validated by protein expression analysis after MAPK blockade. These findings indicate that our functional coating endows dental implants with favorable prospects for enhancing soft tissue sealing, offering a novel strategy to improve the long-term stability of oral implants.
This dataset
Open GSE325639 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 GSE325639_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 PRJNA1441394 and SRA study SRP685858. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 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 9 sample titles: 9 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

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.