The metabolic state can identify cancerous cells and reveal hidden heterogeneities of seemingly identical cells. Conventional methods obfuscate such cellular heterogeneity by averaging many cells. Recently, there has been a great interest to develop new analytical tools for single-cell measurements. However, single-cell mass-spectrometry and fluorescence-based methods are either destructive or can only detect few metabolites simultaneously. I propose to combine two technologies, nanopore-based single-molecule detection of metabolites and patch-clamp of mammalian cells, to develop a system to measure intracellular metabolites in living cells at the single-cell level. I will achieve this objective in three steps: 1) A single cell will be patch-clamped and a Cytolysin A (ClyA) nanopore will be inserted into the cellular membrane. Substrate-binding proteins (SBPs), dwelling inside ClyA, will allow the measurement of cytosolic metabolites which can diffuse from the cell interior into the nanopore. Binding and unbinding of a metabolite to SBPs elicits an ionic current signal that will allow quantification of the metabolite. Because of the single-molecule nature of the system, a wide range of metabolite concentrations can be measured as shown by preliminary data gained from planar lipid bilayer experiments. 2) I will extend the number of SBPs to test a wider range of metabolites. Hundreds of SBPs are reported in the literature with a high structural similarity but high affinities to different molecules. 3) As the system is amenable for parallelization, I will assess intracellular metabolites such as glutathione in myeloma cells whose sensitivity to bortezomib has been suggested to depend on glutathione with a high-throughput (384 cells) system. The system will allow assessing cell-to-cell heterogeneity and single-cell fluctuations with temporal resolutions of seconds, contributing to our understanding of the inner workings of a cell. It will also have wide reaching applications in intracellular pharmacokinetics as well as ex vivo sensing applications.