Optimizing single cell proteomics on a trapped ion mobility spectrometry for label-free experiments

Although single cell RNA-seq has had a tremendous impact on biological research, a corresponding technology for unbiased mass spectrometric analysis of single cells has only recently become available. Significant technological breakthroughs including miniaturized sample handling have enabled proteome profiling of single cells. Further, a trapped ion mobility spectrometry (TIMS) in combination with parallel accumulation-serial fragmentation operated with data-dependant acquisition mode has allowed improved proteome coverage from low-input samples. It has been demonstrated that modulating ion flux in TIMS affects overall performance of proteome profiling. However, the effect of TIMS settings for analyzing low-input samples has been less investigated. Thus, we sought to optimize conditions of TIMS in regards of ion accumulation/ramp times and ion mobility range for low-input samples. We observed that ion accumulation times of 180 ms and monitoring a narrower ion mobility range from 0.7 to 1.3 Vs cm-2 resulted in a substantial gain in the depth of proteome coverage and in detecting proteins with low abundance. We applied these optimized conditions for proteome profiling of sorted human primary T cells, which yielded an average of 365, 804, 1,116, and 1,651 proteins from single, five, ten, and forty T cells, respectively. Notably, we demonstrated that the depth of proteome coverage from low number of cells was sufficient to delineate several essential metabolic pathways and T cell receptor signaling pathway. Finally, we showed the feasibility of detecting post-translational modifications including phosphorylation and acetylation from single cells. We believe that these parameters could be applied to label-free analysis of single cells obtained from clinically relevant samples.