Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

TL;DR

This paper discusses the challenges and opportunities in integrating spatially-resolved transcriptomics data across tissues and individuals, emphasizing the need for standardized methods and scalable algorithms to enhance biological insights.

Contribution

It identifies key challenges in data integration due to varying spatial resolutions and highlights opportunities for developing standardized preprocessing and scalable computational algorithms.

Findings

Highlights challenges of data heterogeneity and resolution differences.

Emphasizes the importance of standardized preprocessing methods.

Suggests scalable algorithms can improve sensitivity and reproducibility.

Abstract

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.