Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue levels

• 成果类型: Article
• 署名作者: Schrom, Edward C.; Mccaffrey, Erin F.; Sreejithkumar, Vivek; Radtke, Andrea J.; Ichise, Hiroshi; Mejias, Armando Arroyo-; Speranza, Emily; Arakkal, Leanne; Thakur, Nishant; Grant, Spencer; Germain, Ronald N.
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); Cleveland Clinic Foundation; National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14577
• DOI: 10.1073/pnas.2412146122
• 发表日期: 2025-02-11
• 关键词: single-cell; gene-expression; landscape; identification; cytometry; reveal

摘要

Spatial patterns of cells and other biological elements drive physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high- plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co- occurrence but also context- dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements-single elements, pairs, triplets, and so on-and ranks the most strongly patterned ensembles. For single images, rankings reflect differences from random assortment. For sets of images, rankings reflect differences across sample the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then detect new patterns across varied datasets, including tumors and tuberculosis granulomas.