Ecological modelling meets spatial biology: a cross-disciplinary toolkit to decode tissue organisation

This project aims to bridge ecology and bioinformatics to provide a novel way of understanding and predicting the stability of cellular ecosystems within tissues—a crucial step toward understanding tissue health and resilience, with broad applications to agriculture, animal and human health. Cells interact dynamically with neighbors and their environment, both locally (e.g., T cells responding to neighbors) and over longer distances (e.g., chemokines guiding immune cells to infection sites). While much research has focused on understanding how individual cells communicate, how these interactions collectively shape tissue behaviour remains poorly understood. This knowledge is crucial for understanding of both healthy tissue function and how tissues break down or transform in disease. Advances in technologies like spatial omics and high-resolution imaging have revolutionized our ability to study these relationships. Spatial omics allows us to map gene expression patterns within tissues, essentially giving us a snapshot of what cells are doing and how they interact across regions. Despite this progress, we still lack robust methods to analyze these interactions in a way that can predict how tissues behave under different conditions. This proposal addresses this gap by creating a new cross-disciplinary approach merging bioinformatics with principles from ecology—a field that has long studied complex systems and interactions. The parallels with ecological research are clear. Ecological communities consist of individuals of many different species that interact with different strengths over restricted distances. Combined with restricted movement, this leads to emergent complex spatial patterns that can tell us much about the underlying biological processes that we do not view directly. This knowledge is crucial to predicting how these species and communities will respond to perturbations like climate shifts. By applying ecological approaches at the cellular level, we will create a toolkit for investigating tissue organization and breakdown under normal and altered conditions to answer questions such as how tissues maintain their structure, how they respond to injuries, and what happens when they undergo changes due to diseases like cancer. Our research has two goals. First, we will develop methods to define cellular communities that function as “mini-ecosystems” within tissues. Ecological communities are often formed of sub-regions typified by certain subsets of species, such as vegetation types across a landscape. These spatially localised communities emerge due to differential interactions between different species within the broader region. Parallel definitions can be used to define cellular communities. By profiling gene activity across tissues, we can map the areas where cell interactions drive emergent behaviour. To identify these areas, we will adapt ecological methods used to map distinct communities in natural environments. Testing these methods in simulated and real datasets will allow us to refine our approach, ensuring it accurately captures cellular communities in various tissue types and organisms. Secondly, we will link these patterns to biological processes and create predictive models that assess how cellular ecosystems respond to disturbances. In natural ecosystems, certain communities can withstand significant changes (like forests recovering after fires), while others may shift to a new state entirely. Similarly, some cellular ecosystems within tissues may be resilient, while others may be prone to breakdown under stressors like aging, injury or disease. By simulating disturbances in these communities—such as removing specific cell types or altering cell-to-cell interactions—we aim to predict the points at which tissues might transition to new, stable states.