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

被引：0
|
作者：
Guo, Boyi [1 ]
Ling, Wodan [2 ]
Kwon, Sang Ho [3 ,4 ,5 ]
Panwar, Pratibha [6 ,7 ,8 ]
Ghazanfar, Shila [6 ,7 ,8 ]
Martinowich, Keri [3 ,4 ,9 ,10 ,11 ]
Hicks, Stephanie C. [1 ,12 ,13 ]
机构：
[1] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD 21205 USA
[2] Weill Cornell Med, Dept Populat Hlth Sci, Div Biostat, New York, NY 10065 USA
[3] Lieber Inst Brain Dev, Johns Hopkins Med Campus, Baltimore, MD 21205 USA
[4] Johns Hopkins Sch Med, Solomon H Snyder Dept Neurosci, Baltimore, MD 21205 USA
[5] Johns Hopkins Univ, Sch Med, Baltimore, MD 21205 USA
[6] Univ Sydney, Sch Math & Stat, Camperdown, NSW 2006, Australia
[7] Univ Sydney, Sydney Precis Data Sci Ctr, Camperdown, NSW 2006, Australia
[8] Univ Sydney, Charles Perkins Ctr, Camperdown, NSW 2006, Australia
[9] Johns Hopkins Sch Med, Dept Psychiat & Behav Sci, Baltimore, MD 21205 USA
[10] Johns Hopkins Univ, Johns Hopkins Kavli Neurosci Discovery Inst, Baltimore, MD 21218 USA
[11] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA
[12] Johns Hopkins Univ, Ctr Computat Biol, Baltimore, MD 21218 USA
[13] Johns Hopkins Univ, Malone Ctr Engn Healthcare, Baltimore, MD 21218 USA
来源：
SMALL METHODS | 2025年
基金：
澳大利亚研究理事会;
关键词：
integrative analysis; multi-sample; population-level; spatial alignment; spatial registration; spatially-resolved transcriptomics; RNA-SEQ DATA; HUMAN CELL ATLAS; SINGLE-CELL; GENE-EXPRESSION; NORMALIZATION; VISUALIZATION; BIOLOGY; IMPACT;
D O I：
10.1002/smtd.202401194
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
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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页数：13
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