Gaining insight into the behavior of bacteria at the single-cell level is important given that heterogeneous microenvironments strongly influence microbial physiology. The hybridization chain reaction (HCR) is a technique that provides molecular signal amplification, enabling simultaneous mapping of multiple target RNAs at small spatial scales. To refine this method for biofilm applications, we designed and validated new probes to visualize the expression of key catabolic genes in Pseudomonas aeruginosa aggregates. In addition to using existing probes for the dissimilatory nitrate reductase (), we developed probes for a terminal oxidase (), nitrite reductase (), nitrous oxide reductase (), and acetate kinase (). These probes can be used to determine gene expression levels across heterogeneous populations such as biofilms. Using these probes, we quantified gene expression across oxygen gradients in aggregate populations grown using the gar lock iofilm ssay (ABBA). We observed distinct patterns of catabolic gene expression, with upregulation occurring in particular ABBA regions both within individual aggregates and over the aggregate population. Aerobic respiration () showed peak expression under oxic conditions, whereas fermentation () showed peak expression in the anoxic cores of high metabolic activity aggregates near the air-agar interface. Denitrification genes and showed peak expression in hypoxic and anoxic regions, although expression remained at peak levels deeper into anoxic environments than other denitrification genes. These results reveal that the microenvironment correlates with catabolic gene expression in aggregates, and they demonstrate the utility of HCR in unveiling cellular activities at the microscale level in heterogeneous populations. To understand bacteria in diverse contexts, we must understand the variations in behaviors and metabolisms they express spatiotemporally. Populations of bacteria are known to be heterogeneous, but the ways this variation manifests can be challenging to characterize due to technical limitations. By focusing on energy conservation, we demonstrate that HCR v3.0 can visualize nuances in gene expression, allowing us to understand how metabolism in Pseudomonas aeruginosa biofilms responds to microenvironmental variation at high spatial resolution. We validated probes for four catabolic genes, including a constitutively expressed oxidase, acetate kinase, nitrite reductase, and nitrous oxide reductase. We showed that the genes for different modes of metabolism are expressed in overlapping but distinct subpopulations according to oxygen concentrations in a predictable fashion. The spatial transcriptomic technique described here has the potential to be used to map microbial activities across diverse environments.