Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shape, relative locations of, movement of, and interactions between cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each that may yield specific insight, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological insight.

翻译：基因组学方法已在多种生物系统中揭示出规律，但未能阐明细胞行为的重要方面：细胞在空间中的形态、相对位置、运动以及相互作用。能够在保留空间信息的同时收集基因组或表观基因组数据的技术已开始突破这些局限。这些新数据有望让我们更深入地理解影响细胞行为的因素，特别是能够直接检验那些此前无法测试的、关于细胞状态及其在形态、位置、运动性和信号传导背景下变异的现有理论。为解答这些问题，空间基因组学技术在分辨率、易用性和规模上的快速进展，也要求我们更新用于分析这些数据的统计学工具库。我们提出了四个可通过空间基因组学数据及相应分析方法解答的开放生物学问题。针对每个问题，我们概述了可能产生具体见解的空间数据模态，探讨了如何通过将数据与生物学行为的概念模型对比来检验相互矛盾的理论，并重点介绍了可能特别有助于恢复生物学见解的统计学和基于机器学习的工具。