When professional phagocytes like macrophages encounter pathogens, they undergo a rapid transformation to promote an inflammatory response. How do we quantify the degree to which these cells become activated? Furthermore, how do we disentangle biological processes whose variability is coupled to inflammatory response versus those which are naturally variable among resting macrophages? As proteins are the agents of cellular activity (the enzymes catalyzing metabolic reactions, the cytokines coordinating responses between cells, etc.), differences in cell states and identities are often characterized by their cellular inventories. In order to conduct an accurate inventory of biomolecules, many techniques are available which are specialized to the biomolecule of interest; for the inventory of cellular proteins, tandem mass spectrometry coupled to liquid chromatography (LC-MS/MS) is a well used technique.
At the outset, our project of conducting a cellular inventory faces a key challenge that LC-MS/MS deals with well: cells can produce a surprising variety of proteins, with some 20,000 protein-coding genes being identified in the human genome. Furthermore, proteins can be ornamented with additional molecules (via phosphorylation, ubiquitination, etc.) or selectively cleaved, and each of these proteoforms may be informative as to the state of the cell. Liquid chromatography allows this complex mixture of analytes to be segmented in time based on physical properties (e.g., hydrophobicity). Mass spectrometry enables ionized biomolecules to be isolated, fragmented, and then identified from the masses of their fragments. Combined, LC-MS/MS enables the sensitive relative quantification of the cellular proteome.
Although there are many flavors of LC-MS/MS-based proteomics, typical high-throughput methods for single-cell samples attempt to best characterize their proteomes through serial analysis of the most highly abundant molecules. Unfortunately, the most highly abundant proteins may not be the most germane to a given study. Additionally, stochasticity in the analysis can result in different subsets of proteins being quantified across experiments. Targeted mass spectrometry methods remediate the abundance bias and stochasticity-induced missing data by providing the instrument with a set of coordinates (the retention time and mass-to-charge ratio) for each analyte of interest. Focusing instrument resources on a limited set of proteins increases the consistency of their identification, but this benefit comes at a cost to total proteome coverage.
In the application of such a method to single-cell samples, we initially struggled to balance these twin considerations of consistency and coverage, but ultimately hit upon a solution: implementing user-defined priority levels for the set of target peptides. We dubbed this method “Prioritized Single-Cell ProtEomics” (pSCoPE). Prioritization preferentially sends high-priority peptides for analysis, while lower-priority peptides are analyzed when instrument time is available. When compared to a typical LC-MS/MS method for multiplexed single-cell analysis, prioritization yielded a 171% increase in consistency of quantification for a set of 1,000 high-priority peptides across single-cell samples. This method also demonstrated a two-fold increase in the number of quantified proteins per single cell. In the analysis of primary murine bone-marrow-derived macrophages (BMDMs), prioritization enabled the quantification of several proteolytically regulated proteoforms whose abundance either varied intrinsically or as a function of treatment with lipopolysaccharide (LPS). Additionally, prioritized analysis identified intrinsically variable biological modules (e.g., Proton Transport and Phagosome Maturation) whose natural variation was unaffected by treatment with an inflammatory stimulus, LPS. We are very excited to share these analyses in our associated publication, as well as further results relating to macrophage endocytic capacity and a benchmark study of the method’s quantitative accuracy.
The prioritized analysis method is implemented as a module within the MaxQuant.Live platform, freely available for download at MaxQuant.Live and scp.SlavovLab.net/pSCoPE. It is our hope that prioritization enables other researchers to more consistently quantify proteoforms of interest across many hundreds to thousands of samples in both single-cell and bulk-level analyses without sacrificing depth of coverage.