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

doi:10.1002/smtd.202401194

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

Small Methods. 2025 May;9(5):e2401194. doi: 10.1002/smtd.202401194. Epub 2025 Feb 11.

Authors

Boyi Guo¹, Wodan Ling², Sang Ho Kwon^{3

4

5}, Pratibha Panwar^{6

7

8}, Shila Ghazanfar^{6

7

8}, Keri Martinowich^{3

4

9

10

11}, Stephanie C Hicks^{1

12

13}

Affiliations

¹ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
² Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 10065, USA.
³ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.
⁴ Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
⁵ Biochemistry, Cellular, and Molecular Biology Graduate Program, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
⁶ School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW, 2006, Australia.
⁷ Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, 2006, Australia.
⁸ Charles Perkins Centre, The University of Sydney, Camperdown, NSW, 2006, Australia.
⁹ Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹⁰ Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21218, USA.
¹¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
¹² Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21218, USA.
¹³ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA.

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.

Keywords: integrative analysis; multi‐sample; population‐level; spatial alignment; spatial registration; spatially‐resolved transcriptomics.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Computational Biology* / methods
Gene Expression Profiling* / methods
Humans
Reproducibility of Results
Transcriptome*

Abstract

Publication types

MeSH terms

Grants and funding