Spatial Flux Balance Analysis reveals region-specific cancer metabolic rewiring and metastatic mimicking

To investigate cancer cell metabolism with spatial precision, it is crucial to estimate the metabolic fluxes with spatial resolution. However, current methods for inferring metabolic fluxes lack spatial resolution at the scale now achievable with spatial transcriptomics. To address this gap, we introduce spatial Flux Balance Analysis (spFBA), a novel framework to process spatial transcriptomics data and compute metabolic fluxes at the spatial spot resolution. The spFBA approach builds upon previous work designed for bulk and single-cell data to return metabolic fluxes, up to the level of single reactions, that can distinguish their preferred directional usage, retaining spatial resolution. spFBA integrates differential constraints on flux boundaries in steady-state metabolic modelling, informed by spatial gene expression, and utilizes a corner-based sampling strategy. We validated spFBA using a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA successfully summarises histological structures and detected cancer metabolic hallmarks, such as enhanced glucose uptake, lactate production, and metabolic growth. Yet, it achieved unprecedented resolution, uncovering region-specific features, including increased glutamate consumption at the tumor interface and hypoxic regions within the tumor core. By applying spFBA to a new stereo-seq colorectal cancer dataset, including paired primary tumor and liver metastasis samples, we provide compelling evidence that metastases mimic the metabolic traits of their tissue of origin. Additionally, we present the first in vivo evidence of lactate-consuming cancer cells. spFBA stands out as a powerful framework to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.