SUMMARY The primary focus of the NIH Compound Identification Development Cores (CIDC) is to use untargeted metabolomics to not only identify novel metabolites but to facilitate and improve the identification of known metabolites. Furthermore, the CIDC is mandated to promote the accuracy, reproducibility, and interlaboratory comparison of metabolomics data. One way of promoting reproducibility, improving comparability and enhancing the confidence of metabolite identification is to improve metabolite quantification -- especially for untargeted metabolomics. Indeed, as frequently shown by untargeted NMR studies, knowledge of the concentration limits of a particular metabolite can “rule-in” or “rule-out” a tentative identification. For instance, if a metabolite signal is tentatively identified as kynurenic acid, but the measured concentration is determined to be 100X times more than normal, then that tentative identification must be incorrect and thus, “ruled out”. Traditionally compound quantification in metabolomics (especially absolute quantification) has been limited to targeted metabolomics while untargeted methods have largely relied on relative quantification. Absolute quantification by LC-MS is difficult and requires isotopically labeled standards and careful calibration. Isotopic standards are expensive and difficult to obtain. As a result, the number of metabolites that can be routinely quantified by targeted LC-MS- based methods is generally less than 500. On the other hand, relative quantification is much easier and it is possible to use peak intensity comparisons between “cases” and “controls” to relatively quantify thousands of compounds with little effort. However, relative quantification has many limitations and numerous problems. In particular, relative values cannot be compared across labs, across platforms, or even over modestly separate time periods within the same lab (batch effects). This makes relative quantification fundamentally “unFAIR” from a data sharing or reproducibility perspective. Furthermore, relative quantification only works for certain limited experimental designs (cases vs. controls) and relative values can never be used in clinical, legal or industrial test settings. This limits the application of untargeted metabolomics to “research-use only”. If untargeted metabolomics is ever going to expand beyond the lab and into the mainstream, it will need to develop robust, label-free quantification methods that can work across different samples, across platforms, across labs and across time. The challenge is how to perform metabolite quantification via LC-MS without isotopic standards? Fortunately, there have been a number of recent developments and novel ideas that integrate both experimental and computation approaches that suggest it may be possible to perform accurate metabolite quantification via untargeted LC-MS metabolomics without isotopically labeled standards. Our goal is to implement, test and refine these met...