PPubMed1 May 2025Cited 12×

Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities.

Boyi Guo, Wodan Ling, Sang Ho Kwon, Pratibha Panwar, Shila Ghazanfar, Keri Martinowich, Stephanie C Hicks

Abstract

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. The lowering cost of SRT data generation presents an unprecedented opportunity to create large-scale spatial atlases and enable population-level investigation, integrating SRT data across multiple tissues, individuals, species, or phenotypes. Here, unique challenges are described in the SRT data integration, where the analytic impact of varying spatial and biological resolutions is characterized and explored. A succinct review of spatially-aware integration methods and computational strategies is provided. Exciting opportunities to advance computational algorithms amenable to atlas-scale datasets along with standardized preprocessing methods, leading to improved sensitivity and reproducibility in the future are further highlighted.

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Integrating spatially-resolved transcriptomics across tissues and individuals poses challenges but enables large‑scale spatial atlases and population‑level insights.

What’s new

i.Describes unique challenges in integrating SRT data across tissues, individuals, species, and phenotypes
ii.Provides a concise review of spatially‑aware integration methods and computational strategies
iii.Highlights opportunities for atlas‑scale algorithms and standardized preprocessing to improve sensitivity and reproducibility

How they did it

The authors performed a literature review of recent spatially‑resolved transcriptomics technologies and computational integration approaches, analyzing how varying spatial and biological resolutions affect integration and synthesizing recommendations for future method development.

What they found

Characterized the analytic impact of varying spatial and biological resolutions on SRT data integration
Summarized existing spatially‑aware integration methods and computational strategies
Identified opportunities for developing atlas‑scale algorithms and standardized preprocessing pipelines

Caveats

✕Current integration methods struggle with differing spatial resolutions across datasets
✕Lack of standardized preprocessing pipelines limits reproducibility
✕Scalability to atlas‑level datasets remains a major challenge

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