Answering open questions in biology using spatial genomics and structured methods

Abstract

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of 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 a framework to respond to this new avenue of research: 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 open question that may yield specific insights, 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 understanding.

Keywords: Biophysics; Cell biology; Machine learning; Spatial genomics; Statistical models.

Fig. 1

Distinct scales of organization at different parts of the body. Subcellular localization of receptors, cytosolic proteins, and signaling molecules affects cellular communication between neurons, B and T cells, or cardiac muscle in the heart. Each of these cell types is, further, a components of multicellular assemblies of many neurons, immune cells in the bloodstream, or heart tissue

Fig. 2

Four key questions in spatial biology. I. Cells can release ligands that allow them to communicate with Other cells across various, unknown spatial scales. II. Cell location can affect morphology, movement of cells within a tissue and gene expression in unknown ways. III. It remains unclear how dividing clonal cells distribute within a tissue, and how this spatial distribution affects dynamics of gene expression. IV. It remains unclear how rare events in gene expression are influenced and orchestrated in within a tissue

Fig. 3

Essential cellular behaviors assayed in spatial genomics. Distributions of RNA (A), cell type clustering from gene expression (B) and spatial correlations (C) can all be measured from spatially resolved sequencing data

Fig. 4

Methodological opportunities for spatial genomics. We describe distinct “classes” of biological and biophysical measurements that fall within our four key areas of interest. These include diffusion of RNA away from the site of transcription, establishment of patterning in a multicellular tissue or organism, and gene regulatory networks giving rise to particular behaviors. For each, we describe how the underlying processes may be directly measured, or indirectly inferred, from spatial genomics data