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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

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Published on: October 31, 2025

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species,

Christina Huan Shi1, Yibo Zhai2, Savio Ho-Chit Chow1

  • 1Center for Data Science and Artificial Intelligence, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States.

Biorxiv : the Preprint Server for Biology
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics data suffers from transcript leakage, where RNA diffuses between cells. Our new method, DeLeakage, effectively corrects this issue, improving data accuracy for cell analysis.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics enables studying gene expression within tissue microenvironments.
  • A prevalent issue of transcript leakage, or RNA diffusion, affects data accuracy across platforms.
  • This leakage impacts gene quantification, cell-type identification, and spatial expression analysis.

Purpose of the Study:

  • To identify and characterize the pervasive transcript leakage problem in spatial transcriptomics data.
  • To develop a novel computational method to mitigate transcript leakage.
  • To demonstrate the effectiveness of the proposed method in improving downstream analyses.

Main Methods:

  • Analysis of diverse spatial transcriptomics datasets to confirm the generality of transcript leakage.
  • Development of a reference-free Bayesian model named DeLeakage.
  • Benchmarking DeLeakage against existing denoising techniques.

Main Results:

  • Transcript leakage is a common artifact across various tissues, species, and spatial transcriptomics technologies.
  • DeLeakage significantly outperforms existing methods in denoising spatial transcriptomics data.
  • The DeLeakage method enhances cell-type annotation accuracy and reduces false positives in spatially dependent gene expression detection.

Conclusions:

  • Transcript leakage is a critical challenge in spatial transcriptomics that requires robust correction.
  • DeLeakage provides an effective solution for mitigating transcript leakage, thereby improving the reliability of spatial transcriptomics analyses.
  • Accurate spatial gene expression data is crucial for understanding tissue architecture and cellular functions.