GEO series

RNA-sequencing analysis of differential expressed genes in TGFβ-treated HK-2 cells after HDAC11 inhibition

GSE280100 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/11/01 Platform GPL11154

At a glance

RNA-seq (gene expression) · human · 15 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: 15 samples: 5 conditions, mostly in triplicateGroups 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)

Renal fibrosis is a pathological damage associated with nearly all forms of kidney disease. Although a recent study has reported an association between upregulation of histone deacetylase 11 (HDAC11) levels in the kidneys and renal fibrosis in several different mouse models, however, the mechanisms remain elusive. Renal fibrosis is characterized by excessive accumulation of extracellular matrix (ECM), which includes type I and IV collagens, fibronectin and α-smooth muscle actin (α-SMA).

Read the full summary ↓

The tubular epithelial injury and activation of pro-inflammation contribute to excessive collagen deposition in the pathogenesis of renal fibrosis. Here we aim to explore the role and the underlying mechanisms of HDAC11 in the of progression renal fibrosis. we performed RNA transcriptome sequencing to identify the differentially expressed genes (DEGs) between before and after HDAC11 inhibitor (FT895)-intreated HK-2 cells. GSEA analysis showed that the DEGs were highly enriched in the pathways of epithelial mesenchymal transition and inflammatory response, indicating that HDAC11 inhibitor may resistant to TGFβ-induced ECM deposition.

This dataset

Open GSE280100 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 GSE280100_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 PRJNA1176271 and SRA study SRP540266. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

5 conditions, mostly in triplicate

HK-2 cells, mock, 0h, ×3 HK-2 cells, TGFb, 1h, ×3 HK-2 cells, TGFb and FT895, 1h, ×3 HK-2 cells, TGFb, 12h, ×3 HK-2 cells, TGFb and FT895, 12h, ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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

GSM8587310 HK-2 cells, mock, 0h, rep1 HK-2 cells, mock, 0h,
GSM8587311 HK-2 cells, mock, 0h, rep2 HK-2 cells, mock, 0h,
GSM8587312 HK-2 cells, mock, 0h, rep3 HK-2 cells, mock, 0h,
GSM8587313 HK-2 cells, TGFb, 1h, rep1 HK-2 cells, TGFb, 1h,
GSM8587314 HK-2 cells, TGFb, 1h, rep2 HK-2 cells, TGFb, 1h,
GSM8587315 HK-2 cells, TGFb, 1h, rep3 HK-2 cells, TGFb, 1h,
GSM8587316 HK-2 cells, TGFb and FT895, 1h, rep1 HK-2 cells, TGFb and F…
GSM8587317 HK-2 cells, TGFb and FT895, 1h, rep2 HK-2 cells, TGFb and F…
GSM8587318 HK-2 cells, TGFb and FT895, 1h, rep3 HK-2 cells, TGFb and F…
GSM8587319 HK-2 cells, TGFb, 12h, rep1 HK-2 cells, TGFb, 12h,
GSM8587320 HK-2 cells, TGFb, 12h, rep2 HK-2 cells, TGFb, 12h,
GSM8587321 HK-2 cells, TGFb, 12h, rep3 HK-2 cells, TGFb, 12h,
GSM8587322 HK-2 cells, TGFb and FT895, 12h, rep1 HK-2 cells, TGFb and F…
GSM8587323 HK-2 cells, TGFb and FT895, 12h, rep2 HK-2 cells, TGFb and F…
GSM8587324 HK-2 cells, TGFb and FT895, 12h, rep3 HK-2 cells, TGFb and F…

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