Mass spectrometry (MS)-based proteomics has tremendous potential to overcome limitations of popular flow cytometry and mass cytometry methods and achieve antibody-independent, comprehensive, and quantitative proteomics analysis of single cells. Such potential has yet to be realized, however, due mainly to ineffective sample processing as well as sample losses prior to MS analysis. To tackle this issue, we recently developed several approaches to significantly improve sample handling and processing, multiplexing, and fractionation, which are being integrated into a robust platform for ultra-sensitive quantitative single-cell proteomics analysis. Individual (i.e. single) cells are first sorted to a nanoPOTS (nanodroplet processing in one pot for trace samples) chip for nearly lossless sample processing, including protein extraction, reduction and alkylation, digestion, and isobaric labeling using 11-plexed tandem mass tags (TMT) reagents, all of which take place in 200-nanoliter droplets and are controlled using a picoliter-resolution robotic system. The resulting samples are then subjected to capillary solid-phase extraction C18 cleanup and nanoscale fractionation and concatenation using a nanoFAC (nanoflow fractionation and automated concatenation) system. An enhanced boosting to amplify signal with isobaric labeling (eBASIL) approach is used for greatly increasing the peptide signal from the single cells. We carefully evaluated and optimized MS data acquisition parameters and potential bias introduced by isotopic impurity of the TMT reagents. Using the optimized conditions, a boosting-to-sample ratio of 1000 can be used to provide ~1500 protein identifications (with no fractionation) without compromising the ability to robustly quantify the proteins in three different acute myeloid leukemia cells. We anticipate the proteome coverage can be further increased with the use of nanoFAC (in progress). We believe this novel integrated platform will enable broad application of single-cell proteomics analysis in biological and biomedical research. It also has the potential to be adapted for phosphoproteome analysis of very small numbers of cells.