Amplification bias in sequencing-based spatial transcriptomics: sources, mechanisms, impacts, and mitigation strategies.

Amplification bias in spatial transcriptomics can distort gene expression data, with errors reaching up to significant levels that hinder accurate spatial analysis.

• Why it matters: Accurate quantification of gene expression in tissue context is crucial for understanding biological processes, but amplification bias undermines data reliability, creating a gap in precise spatial profiling.
• What they did: The review analyzes how input templates, oligonucleotide properties, enzymatic factors, and experimental conditions contribute to bias, and compares strategies for experimental and computational mitigation.
• The result: Implementing optimized mitigation strategies improves data fidelity, enabling more accurate spatial gene expression analysis and advancing the utility of spatial transcriptomics in biological research.