Exploring functional protein covariation across single cells using nPOP

Abstract

Background Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy. Results Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8–20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods. Conclusions nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP.

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

“…Timing and other parameters of the cell-isolation procedure may be impactful and therefore should be recorded so that technical effects associated with sample isolation can be accounted for in downstream analysis. of cell states, which has already benefited from single-cell MS proteomics 6,[16][17][18] . Furthermore, when a large number of single cells are analyzed, the joint distributions of protein abundances enable new types of data-driven analysis (Fig.…”

“…Best practices for single-cell MS proteomics can effectively build on established practices for bulk analysis 23,24 . Common best practices include staggering biological treatments, sample processing and 9 , support pseudotime inference, link protein levels to functional phenotypes, such as phagocytic activity 18 , quantify protein covariation and apply it to study protein complexes 1,6,19 , analyze protein conformations 95 and quantify protein modifications, such as phosphorylation and proteolysis 5,6,18 . Furthermore, integrating protein and RNA measurements from the same biological systems (as in refs.…”

“…Thus, reducing sample-preparation volumes mitigates the effect of contaminant ions originating from reagents such as trypsin or mass tags 2,36 . Indeed, reducing sample-preparation volumes to 2-20 nl proportionally reduces reagent amounts per single cell compared to multiwell-based methods, which in turn reduces the ion current from singly charged contaminant ions 6 .…”

“…New approaches and technologies for experimental design, sample preparation, data acquisition and data analysis have enabled the measurement of several thousand proteins in small subpopulations of cells and even in single mammalian cells [1][2][3][4][5][6][7][8][9][10][11] . These developments open exciting new opportunities for biomedical research 12 , as illustrated in Fig.…”

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

“…6. Using nPOP sample preparation 44 , 10 sets with 3 single cells each were prepared and measured on the Bruker timsTOF platform, resulting in about 1,000 quantified proteins per single cell on average, Fig. 6A.…”