Low cell number proteomic analysis using in-cell protease digests reveals a robust signature for cell cycle state classification

Abstract

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number mass spectrometry (MS)-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the in-cell digest. We combined this with AMPL (Averaged MS1 Precursor Library Matching) to quantitatively characterise proteomes from low cell numbers of human lymphoblasts. 4,500 proteins were detected from 2,000 cells and 2,500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterised the proteome changes across 16 cell cycle states isolated from an asynchronous TK6 human lymphoblast culture, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins (PsPs) that vary across the cell cycle. Clustering of the PsPs showed abundance patterns consistent with waves of protein degradation in late S, at the G2&M border, mid-mitosis and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the APC/C. The dataset also identifies putative APC/C substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high confidence cell cycle regulated proteins can be used to perform unbiased classification of proteomes into cell cycle states. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a cell cycle state for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate this robust cell cycle protein signature will be crucial for classifying cell states in single cell proteomes.