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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...
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Related Experiment Video

Updated: May 13, 2026

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A practical guide to spatial transcriptomics: lessons from over 1000 samples.

Daniela Grases1, Eduard Porta-Pardo2

  • 1Josep Carreras Leukaemia Research Institute, Badalona, Spain.

Trends in Biotechnology
|September 20, 2025
PubMed
Summary

This guide offers practical recommendations for spatial transcriptomics (ST), a powerful gene expression mapping technique. It addresses common challenges to help researchers implement robust and reproducible ST workflows.

Keywords:
VisiumXeniumbest practicesexperimental designspatial omicsspatial transcriptomics

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) maps gene expression within tissues, advancing studies of cellular interactions and organization.
  • Implementation challenges include platform choice, sample quality, and scalability, hindering broader adoption.
  • Over 1000 spatial samples processed across multiple platforms provide a foundation for practical guidance.

Purpose of the Study:

  • To provide a practical guide for spatial transcriptomics (ST) implementation.
  • To address common barriers faced by researchers in adopting ST techniques.
  • To translate hands-on experience into actionable recommendations for robust and reproducible ST workflows.

Main Methods:

  • Review and synthesis of experience from processing over 1000 spatial samples.
  • Development of best practices for experimental design, tissue handling, and sequencing.
  • Guidance on computational analysis tailored for spatial transcriptomics data, including clinical samples.

Main Results:

  • Identification of key practical barriers in spatial transcriptomics implementation.
  • Establishment of best practices for experimental design and sample handling.
  • Recommendations for computational analysis to ensure reproducibility.

Conclusions:

  • This guide translates extensive practical experience into actionable recommendations for spatial transcriptomics.
  • It aims to lower implementation barriers, enabling researchers to establish robust and reproducible spatial workflows.
  • The recommendations support researchers from initial experiments to large-scale ST integration, including clinical applications.