Single-cell sequencing methods have enabled in-depth analysis of the diversity of cell types and cell states in a wide range of organisms. These tools focus predominantly on sequencing the genomes1, epigenomes2 and transcriptomes3 of single cells. However, despite recent progress in detecting proteins by mass spectrometry with single-cell resolution4, it remains a major challenge to measure translation in individual cells. Here, building on existing protocols5-7, we have substantially increased the sensitivity of these assays to enable ribosome profiling in single cells. Integrated with a machine learning approach, this technology achieves single-codon resolution. We validate this method by demonstrating that limitation for a particular amino acid causes ribosome pausing at a subset of the codons encoding the amino acid. Of note, this pausing is only observed in a sub-population of cells correlating to its cell cycle state. We further expand on this phenomenon in non-limiting conditions and detect pronounced GAA pausing during mitosis. Finally, we demonstrate the applicability of this technique to rare primary enteroendocrine cells. This technology provides a first step towards determining the contribution of the translational process to the remarkable diversity between seemingly identical cells.