Quantitative single-cell splicing analysis reveals an ‘economy of scale’ filter for gene expression
Abstract
In eukaryotic cells, splicing affects the fate of each pre-mRNA transcript, helping to determine whether it is ultimately processed into an mRNA, or degraded. The efficiency of splicing plays a key role in gene expression. However, because it depends on the levels of multiple isoforms at the same transcriptional active site (TAS) in the same cell, splicing efficiency has been challenging to measure. Here, we introduce a quantitative single-molecule FISH-based method that enables determination of the absolute abundances of distinct RNA isoforms at individual TASs. Using this method, we discovered that splicing efficiency behaves in an unexpected economy of scale manner, increasing, rather than decreasing, with gene expression levels, opposite to a standard enzymatic process. This behavior could result from an observed correlation between splicing efficiency and spatial proximity to nuclear speckles. Economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. This method will help to reveal the roles of splicing in the quantitative control of gene expression.
02 /Specification & Classification
Click any tag to browse matching papers- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Dynamics of RNA localization to nuclear speckles are connected to splicing efficiency
Jinjun Wu, Yu Xiao, … Jingyi Fei·Science AdvancesPeer-reviewed·Oct 16, 2024
- 3D genome organization around nuclear speckles drives mRNA splicing efficiency
Prashant Bhat, Amy Y. Chow, … Mitchell Guttman·NaturePeer-reviewed·May 8, 2024
- Maximal Efficacy of Alternative Splicing is Ensured by Balanced Efficiency of U1 and U2AF
Jianfei Hu, Daniel C. Douek·bioRxiv·Apr 24, 2025
- Genomic sequences and RNA binding proteins predict RNA splicing kinetics in various single-cell contexts
Ruiyan Hou, Yuanhua Huang·bioRxiv·May 2, 2021
- SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency
V. Costa, Julianus Pfeuffer, … Rosario Michael Piro·bioRxiv·Oct 12, 2020
- noSpliceVelo infers gene expression dynamics without separating unspliced and spliced transcripts
Tarun Mahajan, Sergei Maslov·bioRxiv·Aug 8, 2024