The predominant aim of single cell analysis in clinical research proteomics discovery, companion diagnostics, or personalized medicine research is to decipher the cellular heterogeneity in samples, e.g. tumor tissue. Therefore, dozens to hundreds of individual single cells are required to be analyzed in a relatively short timeframe. Due to recent improvements in mass spectrometry platforms and proteomics sample preparation for single cell as well as fast and robust liquid chromatographic separation, the analysis of single cell proteomes has become an achievable goal.
Human cervical cancer cell digests (HeLa, Pierce) were used to simulate protein concentrations expected at single or few cell level. Further, HEK 293 cells (1 – 20 cells) were sorted and prepared with a CellenOne system (Cellenion). The UPLC systems EvosepOne in Whisper mode (Evosep) and a nanoElute (Bruker) were used and coupled to a timsTOF SCP (Bruker). Data acquisition was done in dia-PASEF mode and data were processed with Dia-NNv1.8 using a predicted human protein sequence library.
Peptides from a HeLa digest were used to firstly assess sensitivity, identification rate and quantification reproducibility. Therefore, 5ng peptide loads were run on 5 different timsTOF SCP. On average 4305 protein groups, 31,000 stripped peptide sequences and 33,000 precursors were identified per instrument with an intra-instrument CV of 1 – 5 % at protein, peptide and precursor identification level. Further, a dilution series (0.125 – 20 ng) demonstrated excellent concentration responses (mean Pearson Correlation score of 0.94) and high identification rates with on average 1250 protein groups and 5600 peptides detected at 125 pg. The analysis of CellenOne sorted and prepared samples revealed several hundreds of protein groups identifiably from a single cell.
In conclusion, we show fast and in-depth proteome quantification with HeLa lysate digest dilutions at concentration range down to single cell level and applied it to real single cell samples.