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| Cheng et al., 2025, Nature Methods |
MAPIT-seq: A New Era in Mapping RNA–Protein Interactions at Single-Cell Resolution
Introduction: Why RNA–Protein Interactions Matter
Inside every cell, RNA-binding proteins (RBPs) work as the master regulators of RNA life. They decide which RNA stays stable, which gets spliced, which travels to a specific corner of the cell, and which is destroyed. Without RBPs, the transcriptome would be like an orchestra without a conductor—chaotic, uncoordinated, and ultimately dysfunctional.
The human genome encodes at least 1,500 RBPs, each playing a role in processes like splicing, localization, translation, and decay. Their influence extends to development, immune responses, aging, and diseases ranging from cancer to neurodegeneration. Disrupting RBP–RNA interactions can trigger devastating consequences—for instance, the RBP G3BP1 is linked to tumor progression, while misregulated RBPs underlie ALS and other neurodegenerative disorders.
So here’s the challenge: how do we map these RNA–protein interactions inside real cells and tissues, with high precision, and ideally, at single-cell resolution?
That’s where MAPIT-seq (Modification Added to RBP Interacting Transcript-sequencing) steps in.
The Long-Standing Bottleneck in Studying RBPs
For years, the field has relied on methods like:
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RIP (RNA Immunoprecipitation) and CLIP (Cross-linking Immunoprecipitation): antibodies pull down RBPs with their RNA partners. While powerful, these approaches are:
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labor-intensive
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low throughput
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prone to non-specific interactions
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and require large amounts of input material (bad news if you’re working with rare cells or tissues).
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TRIBE and STAMP: clever methods that fuse RNA-editing enzymes to RBPs, marking their RNA targets. These work in low-input systems—even single cells—but require genetic manipulation, which isn’t always possible in primary cells or clinical samples.
The bottom line? No method offered single-cell resolution, isoform specificity, and the ability to pair RNA binding data with full transcriptomes from the exact same cell.
Until now.
Enter MAPIT-seq: Antibody-Guided RNA Editing
MAPIT-seq solves these problems with a simple but ingenious trick. Instead of genetically engineering cells to express RBP–enzyme fusions, MAPIT-seq uses antibodies.
Here’s how it works (simplified):
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Fix the cells with mild formaldehyde to freeze RNA–protein interactions in place.
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Add an antibody specific to the RBP you’re studying.
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Recruit a custom fusion protein (pAG-deaminase) that carries two RNA editors:
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These enzymes introduce unique RNA edits near the RBP binding sites.
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Extract RNA and perform sequencing.
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The edits act as molecular breadcrumbs, marking exactly where RBPs were interacting with RNA—while simultaneously giving you the full transcriptome of the same sample.
The result? A dual-omics view: you see both where RBPs bind and how gene expression changes in one streamlined experiment.
Why Dual Editors Matter
One of MAPIT-seq’s innovations is combining two deaminases. Different enzymes prefer different RNA contexts, so by using both ADAR2dd and APOBEC1, the sensitivity and coverage of binding detection improves significantly.
Think of it as photographing a landmark from two angles—you get a clearer picture.
Benchmarking MAPIT-seq: Does It Really Work?
No new method is worth much unless it’s validated. The researchers stress-tested MAPIT-seq against the gold standards:
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YTHDF2 (a well-studied m6A reader): MAPIT-seq editing events aligned neatly with known YTHDF2 CLIP peaks.
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G3BP1: Results overlapped strongly with PAR-CLIP datasets, showing high reproducibility even with different antibodies.
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Other RBPs (PTBP1, RBFOX2, SERBP1, PUM1): MAPIT-seq consistently identified known motifs and binding regions.
In short: MAPIT-seq is not only accurate but also versatile across RBPs.
The PRC2 Puzzle: Do Chromatin Regulators Really Bind RNA?
A fascinating application was reevaluating RNA binding of Polycomb Repressive Complex 2 (PRC2). Some studies claimed PRC2 binds many RNAs, while others disagreed. MAPIT-seq revealed that PRC2 components (EZH2, EED, SUZ12) barely interacted with RNA—except for XIST, a famous long noncoding RNA involved in X-chromosome inactivation.
This finding suggests PRC2 is not a general RNA binder, but instead engages with very specific RNAs under certain conditions. MAPIT-seq thus resolves a decade-long debate with higher clarity.
MAPIT-seq in Action: Mouse Brain Development
Perhaps the most exciting test was applying MAPIT-seq to frozen mouse embryonic brain tissues.
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At embryonic day 12.5 (E12.5), G3BP1 bound RNAs linked to axon growth and early neuronal differentiation.
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At embryonic day 16.5 (E16.5), its targets shifted toward dendrite development and synapse organization.
This showed that the same RBP can play opposite roles at different developmental stages—promoting stability of certain RNAs early, but repressing them later.
Such temporal dynamics would have been impossible to capture with older methods.
scMAPIT-seq: Taking It to the Single-Cell Level
Here’s where things get revolutionary.
The team combined MAPIT-seq with single-cell RNA-seq workflows (like 10x Genomics), enabling scMAPIT-seq. This allowed them to:
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Capture thousands of single cells.
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Map both RBP–RNA interactions and transcriptomes for each individual cell.
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Reveal how binding changes with cell state—for example, G3BP1 had distinct RNA partners in G1, S, and G2/M phases of the cell cycle.
Even more striking: G3BP1 showed opposing regulatory effects—stabilizing some RNAs while destabilizing others—depending on the cell cycle stage.
This is the kind of nuanced, dynamic regulation that bulk methods completely miss.
Long-Read MAPIT-seq: Zooming in on Isoforms
Alternative splicing generates multiple isoforms from a single gene, but most RBP-mapping methods blur them together.
Using PacBio long-read sequencing, MAPIT-seq achieved isoform-level resolution. For example:
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G3BP1 preferentially bound to protein-coding isoforms over intron-retained or non-coding ones.
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It showed stronger binding to longer isoforms, especially those with extended 3′ UTRs.
This opens new doors to understanding how RBPs discriminate between transcript isoforms—a crucial question in both normal physiology and disease.
Advantages of MAPIT-seq Over Other Methods
So why should the RNA community get excited about MAPIT-seq?
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No genetic engineering required → works in primary cells, tissues, even clinical samples.
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Dual editors → higher sensitivity and lower bias.
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Concurrent transcriptome + interactome → directly links binding with expression outcomes.
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Single-cell compatible → captures heterogeneity.
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Isoform resolution → sees binding at the transcript-variant level.
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Scalable and efficient → less laborious than CLIP-based protocols.
In other words: MAPIT-seq is not just an incremental advance, but a genuine leap in RNA biology.
Where Could This Go Next?
The applications are vast:
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Developmental biology: How do RBPs orchestrate lineage decisions?
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Cancer research: Which RBP–RNA interactions drive tumor progression?
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Neurodegeneration: Can we map RBP dysfunction in diseases like ALS or Alzheimer’s?
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Clinical pathology: Since MAPIT-seq works on frozen tissue sections, it could profile RBPs in archived patient samples.
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Therapeutics: If RBPs are drug targets, MAPIT-seq could guide RBP-based precision medicine.
Conclusion: A Framework for the Future
RNA-binding proteins sit at the heart of post-transcriptional regulation, but until now, our tools to study them have been clumsy, biased, or incomplete.
MAPIT-seq changes the game.
By uniting antibody-guided editing with sequencing, it offers:
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a robust, scalable, dual-omics platform,
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applicable to both cultured cells and real tissues,
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extendable to single cells and isoforms.
In essence, MAPIT-seq provides exactly what the field has been waiting for: a comprehensive framework to map RBP regulation in dynamic and clinically relevant contexts.
As more labs adopt it, we may finally crack the code of how RBPs shape the transcriptome—and in turn, how they shape development, disease, and life itself.
Original study:
🔑 Keywords
RNA-binding proteins, RBPs, RNA interactome, RNA–protein interactions, transcriptome regulation, post-transcriptional regulation, MAPIT-seq, scMAPIT-seq, single-cell RNA profiling, isoform-specific RNA binding, RNA editing, ADAR2, APOBEC1, antibody-directed editing, RNA sequencing, multi-omics, dual-omics, PRC2–RNA interactions, XIST lncRNA, G3BP1 regulation, neuronal development, brain transcriptome, RNA splicing, m6A reader, polycomb complex, CLIP-seq alternative, RNA diagnostics, RNA biology, RNA therapeutics, RNA research methods, RNA technology, precision transcriptomics, tissue-based RNA mapping, developmental transcriptomics.
🏷️ Hashtags
#RNA #RBPs #RNASeq #SingleCell #Transcriptomics #MAPITseq #scRNAseq #Epitranscriptomics #MultiOmics #GeneExpression #RNAEditing #PostTranscriptionalRegulation #Neurobiology #BrainDevelopment #RNAResearch #RNAtherapeutics #MolecularBiology #BiotechResearch #RNAtechnology #RNAinnovation #RNAinteractome #NextGenSequencing #RNAtools
