Heme is an essential metabolite for most life on earth. Bacterial pathogens almost universally require iron to infect a host, often acquiring this nutrient in the form of heme. The Gram-negative pathogen is no exception, where heme acquisition and metabolism are known to be crucial for both chronic and acute infections. To unveil unknown genes and pathways that could play a role with heme metabolic flux in this pathogen, we devised an omic-based approach we dubbed "Met-Seq," for abolite-coupled transposon uencing. Met-Seq couples a biosensor with fluorescence-activated cell sorting (FACS) and massively parallel sequencing, allowing for direct identification of genes associated with metabolic changes. In this work, we first construct and validate a heme biosensor for use with and exploit Met-Seq to identify 188 genes that potentially influence intracellular heme levels. Identified genes largely consisted of metabolic pathways not previously associated with heme, including many secreted virulence effectors, as well as 11 predicted small RNAs (sRNAs) and riboswitches whose functions are not currently understood. We verify that five Met-Seq hits affect intracellular heme levels; a predicted extracytoplasmic function (ECF) factor, a phospholipid acquisition system, heme biosynthesis regulator Dnr, and two predicted antibiotic monooxygenase (ABM) domains of unknown function (PA0709 and PA3390). Finally, we demonstrate that PA0709 and PA3390 are novel heme-binding proteins. Our data suggest that Met-Seq could be extrapolated to other biological systems and metabolites for which there is an available biosensor, and provides a new template for further exploration of iron/heme regulation and metabolism in and other pathogens. The ability to simultaneously and more directly correlate genes with metabolite levels on a global level would provide novel information for many biological platforms yet has thus far been challenging. Here, we describe a method to help address this problem, which we dub "Met-Seq" (abolite-coupled Tn uencing). Met-Seq uses the powerful combination of fluorescent biosensors, fluorescence-activated cell sorting (FACS), and next-generation sequencing (NGS) to rapidly identify genes that influence the levels of specific intracellular metabolites. For proof of concept, we create and test a heme biosensor and then exploit Met-Seq to identify novel genes involved in the regulation of heme in the pathogen Met-Seq-generated data were largely comprised of genes which have not previously been reported to influence heme levels in this pathogen, two of which we verify as novel heme-binding proteins. As heme is a required metabolite for host infection in and most other pathogens, our studies provide a new list of targets for potential antimicrobial therapies and shed additional light on the balance between infection, heme uptake, and heme biosynthesis.