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Related Concept Videos

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...
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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Related Experiment Video

Updated: Jun 14, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning.

Minsheng Hao1, Erpai Luo1, Yixin Chen1

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 100084, China.

Communications Biology
|January 6, 2024
PubMed
Summary
This summary is machine-generated.

We developed STEM, a deep learning method to integrate spatial transcriptomics (ST) and single-cell RNA sequencing (SC) data. STEM maps SC data to ST, revealing cellular landscapes at single-cell resolution.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics (ST) lacks single-cell resolution, while single-cell RNA sequencing (SC) lacks spatial information.
  • Integrating ST and SC data is crucial for understanding tissue physiology and pathology at a granular level.

Purpose of the Study:

  • To develop a computational method for integrating ST and SC data.
  • To enable the creation of single-cell resolution spatial transcriptomic landscapes.
  • To uncover the spatial organization and heterogeneity of cells within tissues.

Main Methods:

  • Developed STEM (SpaTially aware EMbedding), a deep transfer learning method.
  • Encoded ST and SC data into a unified, spatially aware embedding space.
  • Inferred SC-ST mapping and predicted pseudo-spatial adjacency for SC data.

Main Results:

  • STEM effectively integrated ST and SC data, preserving spatial information.
  • The method eliminated technical biases between the two data types.
  • Applied to human cancer and liver datasets, STEM identified rare cell localizations and spatial gene expression variations.

Conclusions:

  • STEM enables the construction of single-cell level spatial transcriptomic maps by integrating ST and SC data.
  • This approach provides mechanistic insights into tissue microenvironments and spatial heterogeneity.
  • STEM is a powerful tool for advancing spatial biology research.