GEO series

Inherent Specificity and Variation Sensitivity as Quantitative Metrics for RBP Binding

GSE326416 Homo sapiens (human) Single-nucleus RNA-seq 24 samples Submitted 2026/04/03 Platform GPL18573

At a glance

Single-nucleus RNA-seq · human · 24 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 24 samples: 8 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.
Paper: Inherent Specificity and Variation Sensitivity as Quantitative Metrics for RBP BindingbioRxiv : the preprint server for biology · 2026

Summary (written by the submitters)

Interactions between RNAs and RNA binding proteins (RBPs) regulate gene expression in eukaryotic cells. RNA-RBP affinity measurements in vitro reveal diverse binding preferences, yet approaches to directly compare motif specificities across RBPs are lacking. Here, we introduce two quantitative metrics: inherent specificity, which measures how selectively an RBP distinguishes its strongest binding motif from all possible motifs, and variation sensitivity, which assesses tolerance to single nucleotide variations within a preferred motif.

Read the full summary ↓

Analyzing high-throughput sequencing datasets, we compared these metrics across 100 RBPs in vitro and 27 RBPs in cells, finding a strong correlation between in vitro and cellular measurements for RBPs that bind RNA independently of local structural context. Through RNA binding domain swap CLIP experiments between low-specificity RBM25 and high-specificity HNRNPC, we demonstrated that sequence specificity can be measurably transferred between protein contexts. Using these insights, we developed mathematical models to understand how inherent specificity and mutational sensitivity shape competitive RBP binding in ways not predicted by affinity alone. These models reveal that low-specificity RBPs can functionally enhance the specificity of high-specificity RBPs by occupying non-target sites. Overexpression of low-specificity RBM25 in cells recapitulates this effect, sharpening HNRNPC binding toward its preferred target motifs. Together, this work establishes inherent specificity and variation sensitivity as a quantitative basis for modeling how RBPs compete for RNA targets, complementing affinity-based descriptions of RNA-RBP interactions.

Published in

Inherent Specificity and Variation Sensitivity as Quantitative Metrics for RBP Binding

Yi S, Singh SS, Ye X et al. · bioRxiv : the preprint server for biology 2026 · PMID 42094460 · doi:10.1101/2025.03.28.646018

This dataset

Open GSE326416 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 GSE326416_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1445223 and SRA study SRP687821. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42094460) ↗

Study design

8 conditions, mostly in triplicate

RBM25_WT ×3 RBM25_MUT ×3 HNRNPC_inRBM25_WT ×3 HNRNPC_inRBM25_MUT ×3 HNRNPC_WT_CTRL ×3 HNRNPC_WT_KD ×3 HNRNPC_MUT_CTRL ×3 HNRNPC_MUT_KD ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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

GSM9630659 RBM25_WT_1 RBM25_WT
GSM9630660 RBM25_WT_2 RBM25_WT
GSM9630661 RBM25_WT_3 RBM25_WT
GSM9630662 RBM25_MUT_1 RBM25_MUT
GSM9630663 RBM25_MUT_2 RBM25_MUT
GSM9630664 RBM25_MUT_3 RBM25_MUT
GSM9630665 HNRNPC_inRBM25_WT_1 HNRNPC_inRBM25_WT
GSM9630666 HNRNPC_inRBM25_WT_2 HNRNPC_inRBM25_WT
GSM9630667 HNRNPC_inRBM25_WT_3 HNRNPC_inRBM25_WT
GSM9630668 HNRNPC_inRBM25_MUT_1 HNRNPC_inRBM25_MUT
GSM9630669 HNRNPC_inRBM25_MUT_2 HNRNPC_inRBM25_MUT
GSM9630670 HNRNPC_inRBM25_MUT_3 HNRNPC_inRBM25_MUT
GSM9630671 HNRNPC_WT_CTRL1 HNRNPC_WT_CTRL
GSM9630672 HNRNPC_WT_CTRL2 HNRNPC_WT_CTRL
GSM9630673 HNRNPC_WT_CTRL3 HNRNPC_WT_CTRL
GSM9630674 HNRNPC_WT_KD1 HNRNPC_WT_KD
GSM9630675 HNRNPC_WT_KD2 HNRNPC_WT_KD
GSM9630676 HNRNPC_WT_KD3 HNRNPC_WT_KD
GSM9630677 HNRNPC_MUT_CTRL1 HNRNPC_MUT_CTRL
GSM9630678 HNRNPC_MUT_CTRL2 HNRNPC_MUT_CTRL
GSM9630679 HNRNPC_MUT_CTRL3 HNRNPC_MUT_CTRL
GSM9630680 HNRNPC_MUT_KD1 HNRNPC_MUT_KD
GSM9630681 HNRNPC_MUT_KD2 HNRNPC_MUT_KD
GSM9630682 HNRNPC_MUT_KD3 HNRNPC_MUT_KD

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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.