Identification and relative quantification of hundreds to thousands of proteins within complex biological samples has become realistic with the emergence of stable-isotope labeling in combination with high-throughput mass spectrometry. However, all current chemical approaches are targeting a single amino acid functionality (most often lysine, cysteine), despite the fact that addressing two or more amino acid side chains would drastically increase quantifiable information as shown by in silico analysis in this paper. While the combination of existing approaches, e.g. ICAT with ICPL, is analytically feasible, it implies high costs and the combined application of two different chemistries (kits) may not be straightforward. Therefore, we describe here the development and validation of a new stable-isotope-based quantitative proteomics approach, termed ANIBAL (ANIline Benzoic Acid Labeling), using a twin chemistry approach targeting two frequent amino acid functionalities, the carboxyl- and amino-groups. Two simple and inexpensive reagents – aniline and benzoic acid – in their ¹²C- and ¹³C-form, with convenient mass peak spacing (6 Da) and without chromatographic discrimination or modification in fragmentation behavior, are used to modify carboxylic and amino groups at the protein level, resulting in identical peptide-bond-linked benzoyl modification for both reactions. The ANIBAL chemistry is simple and straightforward and is the first method that uses a ¹³C-reagent for a general stable-isotope labeling approach of carboxylic groups. In silico as well as in vitro analyses clearly revealed the increase in available quantifiable information using such twin approach. ANIBAL has been validated by means of model peptides and proteins with regard to the quality of the chemistry as well as the ionization behavior of the derivatized peptides. A milk fraction was used for dynamic range assessment of protein quantification and a bacterial lysate for the evaluation of relative protein quantification in a complex sample in two different biological states.