GEO series

DNA aptamers that modulate biological activity of model neurons

GSE278187 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/11/11 Platform GPL15520

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)

There is an urgent need for agents that promote health and regeneration of cells and tissues, specifically to treat diseases of the aging nervous system. Age-associated nervous system degeneration and various diseases are driven by many different biochemical stresses, often making it difficult to target any one disease cause. Our laboratory has previously identified DNA aptamers with apparent regenerative properties in murine models of multiple sclerosis by selecting aptamers that bind oligodendrocyte membrane preparations.

Read the full summary ↓

Here, we selected from vast libraries of molecules (~1014 unique DNAs) those with the ability to bind cultured human SH-SY5Y neuroblastoma cells as a neuronal model, followed by screening for aptamers capable of eliciting biological responses, with validation of binding in differentiated SH-SY5Y, human iPSC-derived sensory neurons, and hESC derived cortical neurons. This demonstrates a proof-of-concept workflow to identify biologically active aptamers by cycles of cell selection.

This dataset

Open GSE278187 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 GSE278187_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 PRJNA1165837 and SRA study SRP535142. 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

GSM8541421 LJM3064 treatment replicate A
GSM8541422 LJM3064 treatment replicate B
GSM8541423 LJM3064 treatment replicate C
GSM8541424 LJM6214 treatment replicate A
GSM8541425 LJM6214 treatment replicate B
GSM8541426 LJM6214 treatment replicate C
GSM8541427 LJM6699 treatment replicate A
GSM8541428 LJM6699 treatment replicate B
GSM8541429 LJM6699 treatment replicate C
GSM8541430 LJM7004 treatment replicate A
GSM8541431 LJM7004 treatment replicate B
GSM8541432 LJM7004 treatment replicate C
GSM8541433 LJM7006 treatment replicate A
GSM8541434 LJM7006 treatment replicate B
GSM8541435 LJM7006 treatment replicate C
GSM8541436 LJM7080 treatment replicate A
GSM8541437 LJM7080 treatment replicate B
GSM8541438 LJM7080 treatment replicate C
GSM8541439 LJM7083 treatment replicate A
GSM8541440 LJM7083 treatment replicate B
GSM8541441 LJM7083 treatment replicate C
GSM8541442 LJM7089 treatment replicate A
GSM8541443 LJM7089 treatment replicate B
GSM8541444 LJM7089 treatment replicate C

Datasets on a similar topic

GSE230714 MECP2 directly interacts with RNA polymerase II to modulate transcription in human neurons [RNA-Seq] 12 samples shares modulate, neurons
GSE254741 Human cytomegalovirus infection coopts chromatin organization to modulate TEAD1 transcription factor activity 30 samples shares modulate, activity
GSE279480 Longitudinally stable T cell and innate immune activity distinguish healthy adult immunotypes I 1021 samples shares activity
GSE305224 Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in Hippo-dependent cancer models [RNA-seq] 120 samples shares activity
GSE285217 Digital transformation of herbal medicine: Conversion to biological entity data using Qi related decoction and constituent herbal medicine-induced transcriptome sequencing_third_A549 270 samples shares biological
GSE304585 MAIT and other innate-like T cells integrate adaptive immune responses to modulate interval-dependent reactogenicity to mRNA vaccines 259 samples shares modulate
GSE254732 Inhibition of PCSK9 with evolocumab modulates lipoproteins and monocyte activation in high-risk ASCVD subjects 215 samples shares modulate
GSE280267 Glycine Receptor Activity in β Cells Is Downregulated in Type 2 Diabetes and After High Glucose Culture 199 samples shares activity

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.