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

Abstract

Spatial transcriptomics (ST) has emerged as a powerful approach for profiling gene expression in spatial tissue context; yet, its quantitative accuracy remains substantially compromised by amplification bias introduced during the complex library preparation process. These biases arise at multiple stages and accumulate throughout the experimental workflow, distorting transcript abundance, reducing detection sensitivity, and ultimately confounding downstream spatial analyses. This review systematically analyzes amplification bias in ST. We examine how input templates, oligonucleotide components characteristics, enzymatic properties, and experimental conditions collectively contribute to amplification bias, and discuss how these factors propagate through the workflow to generate systematic distortions in data. We further review and critically compare existing strategies for mitigation, encompassing both experimental optimizations and computational approaches and propose a practical decision framework for selecting amplification-bias mitigation strategies according to platform type, sample quality, and RNA input levels. Finally, we outline key challenges and future directions, emphasizing the need for integrative solutions that jointly consider experimental design and computational modeling. This work provides practical guidance for improving data fidelity and interpretation in ST.